Large scale organoid analysis

ABSTRACT

Methods, systems, and software are provided for using organoid cultures, e.g., patient-derived tumor organoid cultures, to improve treatment predictions and outcomes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/944,292, filed on Dec. 5, 2019, and U.S. Provisional Application No.63/012,885, filed on Apr. 20, 2020, which are expressly incorporated byreference in their entireties for all purposes.

TECHNICAL FIELD

The present disclosure relates generally to large scale organoidanalysis and use of organoids for predicting patient sensitivity totherapeutic agents.

BACKGROUND

Precision oncology is the practice of tailoring cancer therapy to theunique genomic, epigenetic, and/or transcriptomic profile of anindividual tumor. This is in contrast to conventional methods fortreating a cancer patient based merely on the type of cancer the patientis afflicted with, e.g., treating all breast cancer patients with afirst therapy and all lung cancer patients with a second therapy.Precision oncology was borne out of many observations that differentpatients diagnosed with the same type of cancer responded verydifferently to common treatment regimes. Over time, researchers haveidentified genomic, epigenetic, and transcriptomic markers thatfacilitate some level of prediction as to how an individual cancer willrespond to a particular treatment modality.

Therapy targeted to specific genomic alterations is already the standardof care in several tumor types (e.g., as suggested in the NationalComprehensive Cancer Network (NCCN) guidelines for melanoma, colorectalcancer, and non-small cell lung cancer). These few, well known mutationsin the NCCN guidelines can be addressed with individual assays or smallnext generation sequencing (NGS) panels. However, for the largest numberof patients to benefit from personalized oncology, molecular alterationsthat can be targeted with off-label drug indications, combinationtherapy, or tissue agnostic immunotherapy should be assessed. SeeSchwaederle et al. 2016 JAMA Oncol. 2, 1452-1459; Schwaederle et al.2015 J Clin Oncol. 32, 3817-3825; and Wheler et al. 2016 Cancer Res. 76,3690-3701. Large panel NGS assays also cast a wider net for clinicaltrial enrollment. See Coyne et al. 2017 Curr. Probl. Cancer 41, 182-193;and Markman 2017 Oncology 31, 158, 168.

Genomic analysis of tumors is rapidly becoming routine clinical practiceto provide tailored patient treatments and improve outcomes. SeeFernandes et al. 2017 Clinics 72, 588-594. Indeed, recent studiesindicate that clinical care is guided by NGS assay results for 30-40% ofpatients receiving such testing. See Hirshfield et al. 2016 Oncologist21, 1315-1325; Groisberg et al. 2017 Oncotarget 8, 39254-39267; Ross etal. JAMA Oncol. 1, 40-49; and Ross et al. 2015 Arch. Pathol. Lab Med.139, 642-649. There is growing evidence that patients who receivetherapeutic advice guided by genetics have better outcomes. See, forexample Wheler et al. who used matching scores (e.g., scores based onthe number of therapeutic associations and genomic aberrations perpatient) to demonstrate that patients with higher matching scores have agreater frequency of stable disease, longer time to treatment failure,and greater overall survival (2016 Cancer Res. 76, 3690-3701). Suchmethods may be particularly useful for patients who have already failedmultiple lines of therapy.

Targeted therapies have shown significant improvements in patientoutcomes, especially in terms of progression-free survival. See Radovichet al. 2016 Oncotarget 7, 56491-56500. Further, recent evidence reportedfrom the IMPACT trial, which involved genetic testing of advanced stagetumors from 3,743 patients and where approximately 19% of patientsreceived matched targeted therapies based on their tumor biology, showeda response rate of 16.2% in patients with matched treatments versus 5.2%in patients with non-matched treatments. See Bankhead. “IMPACT Trial:Support for Targeted Cancer Tx Approaches.” MedPageToday. Jun. 5, 2018.The IMPACT study further found that the three-year overall survival forpatients given a molecularly matched therapy was more than twice that ofnon-matched patients (15% vs. 7%). See Id. and ASCO Post. “2018 ASCO:IMPACT Trial Matches Treatment to Genetic Changes in the Tumor toImprove Survival Across Multiple Cancer conditions.” The ASCO POST. Jun.6, 2018. Estimates of the proportion of patients for whom genetictesting changes the trajectory of their care vary widely, fromapproximately 10% to more than 50%. See Fernandes et al. 2017 Clinics72, 588-594.

SUMMARY

Given the above background, what is needed in the art are improved waysto identify which cancer patients will respond favorably to therapeuticagents. The present disclosure addresses these and other needs byproviding systems and methods for using organoid cultures to improvetreatment predictions and outcomes. There is also a need for systems andmethods for assessing the effectiveness of various drugs on one or moretumor organoid lines. Such systems or methods may be used to determineif a specific drug may be useful in killing cancer cells with specificgenetic mutations or phenotypes.

Provided herein is a high-throughput drug screening method and systemmore applicable for the unique characteristics of tumor organoids (TOs).The systems and methods provided herein are capable of measuring TOtherapeutic response with high statistical confidence and exquisiteinter-assay reproducibility. This approach can be utilized in researchsettings to elucidate heterogeneity of therapeutic responses within andamong patients, and may be utilized in the clinical laboratory topotentially guide precision oncology treatments. In some embodiments,the platform couples high content fluorescent confocal imaging analysiswith a robust statistical analytical approach to measure hundreds ofdiscrete data points of TO viability from as few as 1×10³ cells. Thisapproach was validated through evaluating responses to hundreds of smallmolecule inhibitors as well as a panel of chemotherapeutic agents in TOmodels derived from different patients.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates a block diagram of an example of a computing devicefor using information derived from organoid-based assays to improvetherapeutic outcomes, in accordance with some embodiments of the presentdisclosure.

FIG. 2 illustrates an example of a distributed diagnostic environmentfor evaluating therapeutic regimes using information derived from tumororganoid studies, in accordance with some embodiments of the presentdisclosure.

FIGS. 3A, 3B, 3C, and 3D illustrate dose response curves for thetreatment of four patient-derived tumor organoid cell lines witholaparib, in accordance with some embodiments of the present disclosure.

FIGS. 4A and 4B collectively show example image of high-contentfluorescent confocal imaging analysis using the subject methodsdescribed herein. (Top panels) Brightfield (top left), Hoechst 33342(top right), Caspase 3/7 (bottom left), and TO-PRO-3 (bottom right)staining is shown for vehicle control (FIG. 4A) and staurosporinetreated (FIG. 4B) gastric cancer TOs. (Bottom panels) Overlays of thefluorescent channels and the result of image analysis are shown side byside. The outline of the larger objects in the right panels indicatesthe area of a given TO. Live and dead cells are shown in the imageanalysis panels. Scale bars represent 100 microns.

FIGS. 5A and 5B illustrate examples of dose-response curves determinedfrom carbocyanine monomer nucleic acid staining (TO-PRO-3; FIG. 5A) andcaspase 3/7 staining (Caspase 3/7; FIG. 5B), in accordance with someembodiments of the present disclosure.

FIG. 6 shows a summary of a study, showing that a classifier trainedusing data sets obtained from the subject methods is capable ofpredicting morphological responses to pharmacological agents in a tumororganoid sample based on a brightfield image of the sample, inaccordance with some embodiments of the present disclosure.

FIGS. 7A and 7B collectively illustrate a neural network-based model forpredicting TO drug response. FIG. 7A shows brighfield, realfluorescence, and generated fluorescence images for colon, lung,ovarian, and breast cancers. FIG. 7B illustrates the correlation betweenthe neural network predictions and fluorescent-based drug responses forthe colon, lung, ovarian, and breast cancers.

FIG. 8 illustrates AUC ROC curves between caspase and generatedviabilities for the PARPi drug screen described in Example 5.

FIGS. 9A and 9B illustrate viability data for a tumor organoid having alow HRD score, a pathogenic BRCA1 mutation, no BRCA2 mutations and noBRCA1 or BRCA2 LOH. The x-axis label indicates the PARP-i therapy anddose. The y-axis indicates the percent of cells in the well that wereviable, normalized to the DMSO mock condition (negative control).

FIGS. 10A and 10B illustrate viability data for a colorectal tumororganoid having a high HRD score, no BRCA1 or BRCA2 mutations and noBRCA1 or BRCA2 LOH. The x-axis label indicates the PARP-i therapy anddose. The y-axis indicates the percent of cells in the well that wereviable, normalized to the DMSO mock condition (negative control).

FIGS. 11A and 11B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 12A and 12B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 13A and 13B illustrate viability data for a tumor organoid havinga high HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 14A and 14B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations, BRCA1 LOH, and no BRCA2LOH. The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 15A and 15B illustrate viability data for a tumor organoid havinga high HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control). Lower doses of PARPi weremore effective for this organoid than the low HRD shown in the previousfigures organoid.

FIGS. 16A and 16B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 17A and 17B illustrate viability data for a tumor organoid havinga high HRD score, no BRCA1 or BRCA2 mutations, no BRCA1 LOH and havingBRCA2 LOH. The x-axis label indicates the PARP-i therapy and dose. They-axis indicates the percent of cells in the well that were viable,normalized to the DMSO mock condition (negative control).

FIGS. 18A and 18B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The y-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 19A-19E provide a summary of a study disclosed herein for organoiddevelopment via a chemically defined pan-cancer approach. A:Illustration of workflow for generating TOs for multiple downstreamassays. B: Representative H&E staining of 10 TO histologic types. C:Percent of established and high-proliferation TOs in prevalenthistologic cancer types. D: Growth rate (log₁₀ μm²/day) of TOs fromprevalent cancer types. E: Exemplary media used to culture the tumororganoids described herein.

FIGS. 20A-20F show the genomic and transcriptomic concordance betweenTOs and source tumors. A: Somatic mutation landscape. Genes presentedwere the top 10 most frequently mutated genes. For each gene, the toprow represents source tumor mutations and the bottom row represents TOmutations. B: Somatic recapitulation and CNV concordance across cancers.Note—x-axis range is from 0.4 to 1.0. C: VAF correlation ofrecapitulated variants. Tumor VAF was adjusted for tumor purity. Pointsare colored by cancer type. For A-C, variants included were classifiedas pathogenic, likely pathogenic or of unknown significance. D:Transcriptome correlation between source tumors and tumor organoids.Unpaired correlation represents all other pairwise comparisons betweentumors and organoids. E: Representative copy-number plots depicting acolon cancer tumor and corresponding patient-derived TO. Majorcopy-number alterations >3 megabases (Mb) are depicted as blocks andcopy-number alterations <3 Mb are depicted as points. Highlighted is aregion on chromosome 6 that is depicted as having a normal copy numberin the sequenced tumor, but a predicted loss of heterozygosity in thepaired patient derived TO. F: (Top) Three paired panels of Tempus xTsequencing coverage in the HLA-A locus (by normal, source tumor, andpatient-derived TO). (Bottom) positive control (non-patient paired,genotype-matched (HLA-A02 positive) PBMC; negative control (unrelatedHLA-A02 negative) PBMC; and patient-derived TO) fluorescence-activatedcell sorting (FACS) data by panMHC and HLA-A02 antibodies.

FIGS. 21A-21E show that tumor organoids recapitulate patient tumormolecular landscapes. A: Pan-cancer somatic mutational landscapes inTempus xT-sequenced tumors (n=292) and TOs (n=230). The top 20 mostaltered genes (by row) are represented across cancer types (by color).Variants included were classified as pathogenic, likely pathogenic or ofunknown significance. B: Transcriptome PCA of 177 TOs exhibitsclustering by cancer type. C: Clustered heatmap of differentiallyactivated hallmark ssGSEA pathways between tumors and TOs. Presentedpathways were differentially activated (P<0.01) in at least one of thesix most represented cancer types. Color scale indicates odds ratio (OR)of difference, with red color indicating higher activation in the tumororganoid and blue indicating lower activation. D. Concordance betweenpresence of Wnt and P53 pathway-disrupting mutations and RNA-basedpathway disruption in TOs. Plots represent ssGSEA scores for therelevant MSigDB hallmark pathway in TOs with and without pathogenic orlikely pathogenic mutations in APC in (Wnt pathway) and TP53 (P53signaling pathway). All cancer types are represented. E. Estrogenpathway activation based on ssGSEA score in indicated cancer/cancergroup for MSigDB hallmark pathway Estrogen Response Early.

FIGS. 22A-22G summarize a study showing pan-cancer niche factorrequirements in tumor organoid cultures. A: Workflow and timeline of TOniche factor profiling and media formulations. Bold plus signs indicatethe presence of growth factors in each media type. B: Representativetime-lapse images of colon TO growth in varying media formulations.Scale bar=70 μm. C: Heatmap of the growth of ten colon TO lines invarying media formulations. Data are represented as the maximum growthrate normalized to type B (mitogen free) media. D: Heatmap of the growthof 45 TO lines in varying media formulations. Data are represented asthe maximum growth rate normalized to type B (mitogen free) media.*P<0.01 by ANOVA. E: Growth curve showing change of mean Pancreatic TOarea over time in various media conditions. F: PCA of wholetranscriptomes from 8 TO lines grown in various media conditions. G:Barplot of establishment of 100 sequential TO cultures in minimal vs.standard chemically defined media.

FIGS. 23A-23D provide a summary of a study for the development of alabel-free universal organoid drug screening assay. A: Experimentaldesign of the drug assays. B: Representative images from high-contentimaging analysis of TOs treated with either vehicle (0.1% DMSO) or 10 μMStaurosporine. Fluorescent labeling (top row) was used to identify allcells (blue, Hoechst 33342), apoptotic cells (green, Caspase-3/7), anddead cells (red, TO-PRO-3). Scale bar=200 μm. C: Example dose-responsecurve for staurosporine for both the cystic TO (CRC) and solid TO(gastric) lines for TO viability calculated from TO-PRO-3, Caspase-3/7,and live cells per TO. The mean viability (or number of live cells perTO) was calculated for each well and normalized to the mean of thevehicle control. Points indicate mean±SD of technical triplicate wells.D: Heatmaps show the inverse AUC for all 351 compounds for both thegastric and CRC TOs for each readout. Dose-response curves were used tocalculate the inverse AUC for each drug.

FIGS. 24A-24G depict a summary of a study for the development of aneural network-based model for predicting TO drug response. A: Schematicrepresentation of Regularized Conditional Adversarial Networkarchitecture showing dual predictive ability. Fluorescent Generator(Left) generates a fluorescence registered readout for every inputbrightfield image. Viability Discriminator (Right) predicts an overallTO viability for the paired brightfield and generated fluorescencereadout. B: Representative images highlighting the brightfield, realfluorescence and generated fluorescence readouts from the CRC TO. Scalebar=200 μm. C: Scatter plots showing the relationship between groundtruth (TO-PRO-3) and predictive average TO-PRO-3 viabilities on imagesbelonging to CRC TO (not included in the training set) and Gastric TO. Dand E: Compounds were grouped by their reported targets and are shownranked by median inverse AUC values calculated from dose-response curvesof RCA generated viability values for the CRC (D) and gastric (E) TOs.Dose-response curves for trametinib (D) and afatinib (E) highlightingthe correlation between generated, TO-PRO-3 and Caspase-3/7 viabilities.F: Copy-number amplification plot for the Gastric TO exhibiting ERBB2amplification (arrow). G: AUC ROC curves between fluorescent andgenerated viabilities to assess sensitivity and specificity of PARPiresponse to classify organoids as HRD positive or HRD negative asdetermined by genome-wide LOH proportion (Tempus HRD assay).

FIG. 25A-25C are histograms summarizing a therapeutic agent screen for agastric cancer tumor organoid line according to the methods describedherein.

FIG. 26A-26D are histograms summarizing a therapeutic agent screen for alung cancer tumor organoid line according to the methods describedherein.

FIG. 27A-27D are histograms summarizing a therapeutic agent screen for alung cancer tumor organoid line according to the methods describedherein.

FIG. 28A-28B are histograms summarizing a therapeutic agent screen for ahead and neck cancer tumor organoid line according to the methodsdescribed herein.

FIG. 29A-29C are histograms summarizing a therapeutic agent screen foran endometrial cancer tumor organoid line according to the methodsdescribed herein.

FIG. 30A-30C are histograms summarizing a therapeutic agent screen foran endometrial cancer tumor organoid line according to the methodsdescribed herein.

FIG. 31A-31C are histograms summarizing a therapeutic agent screen for acolon cancer tumor organoid line according to the methods describedherein.

FIG. 32A-32B are histograms summarizing a therapeutic agent screen for acolorectal cancer tumor organoid line according to the methods describedherein.

FIG. 33 shows an example of a system for automatically analyzing tumororganoid images.

FIG. 34 shows an example of hardware that can be used in someembodiments of the system.

FIG. 35 shows an exemplary flow that can generate brightfield imagesand/or fluorescent images, as well as live/dead assays readouts, usingpatient derived organoids grown from tumor specimens.

FIG. 36 shows an exemplary flow for training a generator to generate anartificial fluorescent image based on an input brightfield image oforganoid cells.

FIG. 37 shows an exemplary flow for generating an artificial fluorescentimage.

FIG. 38 shows an exemplary neural network.

FIG. 39 shows an exemplary discriminator.

FIG. 40 shows an exemplary process that can train a model to generate anartificial fluorescent stain image of one or more organoids based on aninput brightfield image.

FIG. 41 shows an exemplary process that can generate an artificialfluorescent image of one or more organoids based on a brightfield image.

FIG. 42 shows exemplary raw images before preprocessing and afterpreprocessing.

FIG. 43 shows an exemplary flow for culturing tumor organoids. Cultureof patient derived tumor organoids.

FIG. 44 shows an exemplary flow for conducting drug screens inaccordance with systems and methods described herein.

FIG. 45 shows an exemplary process that can generate artificialfluorescent images at multiple time points for at least one organoid.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

In some embodiments, the present disclosure provides systems and methodsfor using organoid cultures, e.g., patient-derived tumor organoidcultures, to improve treatment predictions and outcomes.

For instance, in one aspect, the disclosure provides methods and systemsfor performing methods of training a classifier to discriminate betweentwo or more tumor sensitivities to one or more therapeutic agents. Insome embodiments, the method includes obtaining a data set comprising,for each respective tissue sample in a plurality of tissue samples, (i)a corresponding plurality of nucleic acid features of the tissue sample,and (ii) a corresponding indication of the sensitivity of a respectiveorganoid cultured from one or more cells of the tissue sample to the oneor more therapeutic agents. The method then includes training anuntrained classifier against at least (i) the corresponding plurality offeatures and (ii) the corresponding indication of the sensitivity of theorganoid to the one or more therapeutic agents, across the plurality oftissue samples, thereby obtaining a trained classifier thatdiscriminates between the two or more tumor sensitivities to one or moretherapeutic agents.

In another aspect, the disclosure provides method and systems forperforming methods of recommending therapy for treating a cancer in asubject. In some embodiments, the method includes obtaining a first testdata set comprising a plurality of nucleic acid features of a tumorbiopsy from the test subject. The method then includes evaluating thefirst test data set using a classifier trained to discriminate betweentwo or more tumor sensitivities to a first therapeutic agent. Theclassifier is trained against, for each respective training tissuesample in a plurality of training tissue samples, at least (i) theplurality of nucleic acid features obtained from the respective trainingtissue sample, and (ii) a corresponding indication of the sensitivity ofa respective organoid cultured from one or more cells of the respectivetraining tissue sample to the first therapeutic agent. In someembodiments, the sensitivity of the training tissue sample is determinedin a two-dimensional cell culture, a three-dimensional cell cultureprepared in a matrix, a three-dimensional cell culture prepared insuspension culture, a cell culture suspension, or a xenograft model. Afirst tumor sensitivity in the two or more tumor sensitivities to thefirst therapeutic agent is associated with a first recommendation, in aplurality of recommendations, to treat the cancer in the subject withthe first therapeutic agent. A second tumor sensitivity in the two ormore tumor sensitivities to the first therapeutic agent is associatedwith a second recommendation, in the plurality of recommendations, notto treat the cancer in the subject with the first therapeutic agent. Insome embodiments, the method includes providing a respectiverecommendation, in the plurality of recommendations, for treating thecancer in the test subject based on the results of the evaluation.

In another aspect, the disclosure provides methods and systems forperforming methods of treating, or assigning therapy for, a cancer in atest subject. In some embodiments, the methods include determining aprobability or likelihood that the cancer will be sensitive to atherapeutic agent, and treating cancer in the subject. When theprobability or likelihood that the cancer will be sensitive to thetherapeutic agent satisfies a sensitivity threshold, the method includesadministering, or communicating, a recommended therapy comprising thetherapeutic agent to the test subject. When the probability orlikelihood that the cancer will be sensitive to the therapeutic agentdoes not satisfy a sensitivity threshold, the method includesadministering, or communicating, a recommended therapy that does notinclude the therapeutic agent to the test subject.

In another aspect, the disclosure provides methods and systems forperforming methods of providing a clinical report for a cancer patientto a physician. In some embodiments, the methods include obtaining afirst test data set comprising features of a transcriptome from a tumorbiopsy from the cancer patient. In some embodiments, the methods includeevaluating the first test data set using a classifier trained, e.g., asdescribed herein, to discriminate between two or more tumorsensitivities to a first therapeutic agent. The methods then includereceiving a recommended therapy for the cancer patient from theclassifier, and including the recommended therapy, or sending therecommended therapy to a third party for inclusion, in a clinical reportfor the cancer patient.

In another aspect, the disclosure provides methods and systems forperforming methods of identifying a new use for a pharmaceuticalcompound of a first pharmaceutical class. In some embodiments, themethods include obtaining a plurality of tissue samples, wherein eachrespective tissue sample in the plurality of tissue samples is sensitiveto a respective class of pharmaceutical agents in a plurality of classesof pharmaceutical agents that excludes the first pharmaceutical class.The methods then includes culturing, for each respective tissue samplein the plurality of tissue samples, one or more respective organoidsfrom one or more cells of the respective tissue sample, therebygenerating a plurality of organoid cultures. The methods then includeexposing, for each organoid culture in the plurality of organoidcultures, the respective one or more organoids to one or moreconcentrations of the pharmaceutical agent, and measuring, for eachorganoid culture in the plurality of organoid cultures, the fitness ofcells in the respective one or more organoids following the exposure tothe pharmaceutical agent. Reduced fitness of the cells in the respectiveone or more organoids is indicative that the pharmaceutical moleculeshares pharmacological properties with the class of pharmaceuticalcompounds in the plurality of classes of pharmaceutical classes to whichthe tissue sample corresponding to the respective one or more organoidsis sensitive.

In another aspect, the disclosure provides methods and systems forperforming methods of determining the eligibility of a cancer patientfor a clinical trial of a candidate cancer pharmaceutical agent. In someembodiments, the methods include obtaining a tumor biopsy from thecancer patient, and culturing one or more tumor organoids from one ormore cells of the tumor biopsy. The methods then include exposing theone or more tumor organoids to one or more concentrations of thecandidate cancer pharmaceutical agent, and measuring the fitness ofcells in the one or more tumor organoids following the exposure to theone or more concentrations of the candidate cancer pharmaceutical agent.The methods then include determining whether the cancer patient iseligible for the clinical trial based on at least the measured fitnessof the cells in the one or more tumor organoids, wherein reduced fitnessof the cells in the one or more tumor organoids is indicative that thecancer patient is eligible for the clinical trial.

In another aspect, the disclosure provides methods and systems forperforming methods of determining the eligibility of a cancer patientfor a clinical trial of a candidate cancer pharmaceutical agent. In someembodiments, the methods include obtaining a first test data setcomprising a plurality of nucleic acid features of a tumor biopsy fromthe cancer patient. The methods then include evaluating the first testdata set using a classifier trained to discriminate between two or moretumor sensitivities to the candidate cancer pharmaceutical agent. Theclassifier is trained against, for each respective training tissuesample in a plurality of training tissue samples, at least (i) theplurality of nucleic acid features obtained from the respective trainingtissue sample, and (ii) a corresponding indication of the sensitivity ofa respective organoid cultured from one or more cells of the respectivetraining tissue sample to the candidate cancer pharmaceutical agent. Afirst tumor sensitivity in the two or more tumor sensitivities to thecandidate cancer pharmaceutical agent is associated with an indicationthat the cancer patient is eligible for the clinical trial. A secondtumor sensitivity in the two or more tumor sensitivities to thecandidate cancer pharmaceutical agent is associated with an indicationthat the cancer patient is eligible for the clinical trial. The methodsthen include determining whether the cancer patient is eligible for theclinical trial based on at least the evaluation.

In another aspect, the disclosure provides methods and systems forperforming methods of identifying a resistance to a chemotherapeuticagent in a patient with cancer. In some embodiments, the methods includeobtaining a first test data set comprising a plurality of nucleic acidfeatures of a tumor biopsy from the patient with cancer. The methodsthen include evaluating the first test data set using a classifiertrained to discriminate between two or more tumor sensitivities to thechemotherapeutic agent. The classifier is trained against, for eachrespective training tissue sample in a plurality of training tissuesamples, at least (i) the plurality of nucleic acid features obtainedfrom the respective training tissue sample, and (ii) a correspondingindication of the sensitivity of a respective organoid cultured from oneor more cells of the respective training tissue sample to thechemotherapeutic agent. A first tumor sensitivity in the two or moretumor sensitivities to the candidate cancer pharmaceutical agent isassociated with an indication that the patient's cancer is resistant tothe chemotherapeutic agent. A second tumor sensitivity in the two ormore tumor sensitivities to the candidate cancer pharmaceutical agent isassociated with an indication that the patient's cancer is not resistantto the chemotherapeutic agent. The methods then include providing areport indicating whether the patient's cancer is resistant to thechemotherapeutic agent based on at least the results of the evaluation.

In another aspect, the disclosure provides methods and systems forevaluating an effect of a cancer therapeutic agent. In some embodiments,the method includes providing a plurality of tumor organoids cultured ina tumor organoid culture medium, where the plurality of tumor organoidsis divided into a plurality of tumor organoid subsets. The method thenincludes contacting each subset in the plurality of tumor organoidsubsets with a cancer therapeutic agent. The method also includescontacting each subset in the plurality of tumor organoid subsets withone or more cell death detection agents and a total cell detectionagent. The method then includes obtaining a tumor organoid profile foreach subset in the plurality of subset. In some embodiments, the tumororganoid profile includes a cell viability value for every tumororganoid in the subset. The method then includes assessing the effect ofthe cancer therapeutic agent based on the tumor organoid profiles.

In another aspect, the disclosure provides methods and systems forassigning a treatment dosage of a cancer therapeutic agent for a subjectin need thereof. In some embodiments, the method includes providing aplurality of tumor organoids cultured in a tumor organoid culturemedium, where the tumor organoids are derived from a subject, and wherethe plurality of tumor organoids is divided into a plurality of tumororganoid subsets. The method then includes contacting each subset in theplurality of tumor organoid subsets with a different dosage of a cancertherapeutic agent. The method also includes contacting each subset inthe plurality of tumor organoid subsets with one or more cell deathdetection agents and a total cell detection agent. The method thenincludes obtaining a tumor organoid profile for each subset in theplurality of subset, where the tumor organoid profile includes a cellviability value for every tumor organoid in the subset. The method thenincludes determining a therapeutic agent dosage curve from the tumororganoid profiles. The method then includes assigning a treatment dosageof the cancer therapeutic agent to the subject based on the therapeuticagent dosage curve.

A. Definitions

The terminology used in the present disclosure is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the invention. As used in the description of the inventionand the appended claims, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will also be understood that the term “and/or”as used herein refers to and encompasses any and all possiblecombinations of one or more of the associated listed items. It will befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. Furthermore, to the extent that the terms “including,”“includes,” “having,” “has,” “with,” or variants thereof are used ineither the detailed description and/or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first subject could be termed asecond subject, and, similarly, a second subject could be termed a firstsubject, without departing from the scope of the present disclosure. Thefirst subject and the second subject are both subjects, but they are notthe same subject. Furthermore, the terms “subject,” “user,” and“patient” are used interchangeably herein.

As used herein, the term “organoid” refers to an in vitrothree-dimensional multicellular construct that is developed from stemcells (e.g., embryonic stem cells, induced pluripotent stem cells, andsomatic stem cells or tissue derived progenitor cells) or cancer cellsin a specific 3D organoid culture system. Organoids contain multiplecells types of the in vivo counter parts and organize similarly to theprimary tissue. In some embodiments, the organoid culture systemincludes an organoid culture medium and an extracellular matrix orextracellular matrix substitute. An “organoid cell line” refers to aplurality of organoids that are derived and established from the samecell or cell population. A “tumor organoid” refers to an organoidderived from a tumor cell or population of tumor cells.

As used herein, the term “subject” refers to any living or non-livinghuman. In some embodiments, a subject is a male or female of any stage(e.g., a man, a women or a child).

As used herein, the terms “control,” “control sample,” “reference,”“reference sample,” “normal,” and “normal sample” describe a sample froma subject that does not have a particular condition, or is otherwisehealthy. In an example, a method as disclosed herein can be performed ona subject having a tumor, where the reference sample is a sample takenfrom a healthy tissue of the subject. A reference sample can be obtainedfrom the subject, or from a database. The reference can be, e.g., areference genome that is used to map sequence reads obtained fromsequencing a sample from the subject. A reference genome can refer to ahaploid or diploid genome to which sequence reads from the biologicalsample and a constitutional sample can be aligned and compared. Anexample of constitutional sample can be DNA of white blood cellsobtained from the subject. For a haploid genome, there can be only onenucleotide at each locus. For a diploid genome, heterozygous loci can beidentified; each heterozygous locus can have two alleles, where eitherallele can allow a match for alignment to the locus.

As used herein, the term “locus” refers to a position (e.g., a site)within a genome, e.g., on a particular chromosome. In some embodiments,a locus refers to a single nucleotide position within a genome, i.e., ona particular chromosome. In some embodiments, a locus refers to a smallgroup of nucleotide positions within a genome, e.g., as defined by amutation (e.g., substitution, insertion, or deletion) of consecutivenucleotides within a cancer genome. Because normal mammalian cells havediploid genomes, a normal mammalian genome (e.g., a human genome) willgenerally have two copies of every locus in the genome, or at least twocopies of every locus located on the autosomal chromosomes, e.g., onecopy on the maternal autosomal chromosome and one copy on the paternalautosomal chromosome.

As used herein, the term “allele” refers to a particular sequence of oneor more nucleotides at a chromosomal locus.

As used herein, the term “reference allele” refers to the sequence ofone or more nucleotides at a chromosomal locus that is either thepredominant allele represented at that chromosomal locus within thepopulation of the species (e.g., the “wild-type” sequence), or an allelethat is predefined within a reference genome for the species.

As used herein, the term “variant allele” refers to a sequence of one ormore nucleotides at a chromosomal locus that is either not thepredominant allele represented at that chromosomal locus within thepopulation of the species (e.g., not the “wild-type” sequence), or notan allele that is predefined within a reference genome for the species.

As used herein, the term “single nucleotide variant” or “SNV” refers toa substitution of one nucleotide to a different nucleotide at a position(e.g., site) of a nucleotide sequence, e.g., a sequence read from anindividual. A substitution from a first nucleobase X to a secondnucleobase Y may be denoted as “X>Y.” For example, a cytosine to thymineSNV may be denoted as “C>T.”

As used herein, the term “mutation” or “variant” refers to a detectablechange in the genetic material of one or more cells. In a particularexample, one or more mutations can be found in, and can identify, cancercells (e.g., driver and passenger mutations). A mutation can betransmitted from apparent cell to a daughter cell. A person having skillin the art will appreciate that a genetic mutation (e.g., a drivermutation) in a parent cell can induce additional, different mutations(e.g., passenger mutations) in a daughter cell. A mutation generallyoccurs in a nucleic acid. In a particular example, a mutation can be adetectable change in one or more deoxyribonucleic acids or fragmentsthereof. A mutation generally refers to nucleotides that is added,deleted, substituted for, inverted, or transposed to a new position in anucleic acid. A mutation can be a spontaneous mutation or anexperimentally induced mutation. A mutation in the sequence of aparticular tissue is an example of a “tissue-specific allele.” Forexample, a tumor can have a mutation that results in an allele at alocus that does not occur in normal cells. Another example of a“tissue-specific allele” is a fetal-specific allele that occurs in thefetal tissue, but not the maternal tissue.

As used herein the term “cancer,” “cancerous tissue,” or “tumor” refersto an abnormal mass of tissue in which the growth of the mass surpassesand is not coordinated with the growth of normal tissue. A cancer ortumor can be defined as “benign” or “malignant” depending on thefollowing characteristics: degree of cellular differentiation includingmorphology and functionality, rate of growth, local invasion andmetastasis. A “benign” tumor can be well differentiated, havecharacteristically slower growth than a malignant tumor and remainlocalized to the site of origin. In addition, in some cases a benigntumor does not have the capacity to infiltrate, invade or metastasize todistant sites. A “malignant” tumor can be a poorly differentiated(anaplasia), have characteristically rapid growth accompanied byprogressive infiltration, invasion, and destruction of the surroundingtissue. Furthermore, a malignant tumor can have the capacity tometastasize to distant sites. Accordingly, a cancer cell is a cell foundwithin the abnormal mass of tissue whose growth is not coordinated withthe growth of normal tissue. Accordingly, a “tumor sample” refers to abiological sample obtained or derived from a tumor of a subject, asdescribed herein.

As used herein, the terms “sequencing,” “sequence determination,” andthe like as used herein refers generally to any and all biochemicalprocesses that may be used to determine the order of biologicalmacromolecules such as nucleic acids or proteins. For example,sequencing data can include all or a portion of the nucleotide bases ina nucleic acid molecule such as an mRNA transcript or a genomic locus.

As used herein, the term “sequence reads” or “reads” refers tonucleotide sequences produced by any sequencing process described hereinor known in the art. Reads can be generated from one end of nucleic acidfragments (“single-end reads”), and sometimes are generated from bothends of nucleic acids (e.g., paired-end reads, double-end reads). Thelength of the sequence read is often associated with the particularsequencing technology. High-throughput methods, for example, providesequence reads that can vary in size from tens to hundreds of base pairs(bp). In some embodiments, the sequence reads are of a mean, median oraverage length of about 15 bp to 900 bp long (e.g., about 20 bp, about25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp,about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110bp, about 120 bp, about 130, about 140 bp, about 150 bp, about 200 bp,about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, orabout 500 bp. In some embodiments, the sequence reads are of a mean,median or average length of about 1000 bp, 2000 bp, 5000 bp, 10,000 bp,or 50,000 bp or more. Nanopore sequencing, for example, can providesequence reads that can vary in size from tens to hundreds to thousandsof base pairs. Illumina parallel sequencing can provide sequence readsthat do not vary as much, for example, most of the sequence reads can besmaller than 200 bp. A sequence read (or sequencing read) can refer tosequence information corresponding to a nucleic acid molecule (e.g., astring of nucleotides). For example, a sequence read can correspond to astring of nucleotides (e.g., about 20 to about 150) from part of anucleic acid fragment, can correspond to a string of nucleotides at oneor both ends of a nucleic acid fragment, or can correspond tonucleotides of the entire nucleic acid fragment. A sequence read can beobtained in a variety of ways, e.g., using sequencing techniques orusing probes, e.g., in hybridization arrays or capture probes, oramplification techniques, such as the polymerase chain reaction (PCR) orlinear amplification using a single primer or isothermal amplification.

As used herein, the term “read segment” or “read” refers to anynucleotide sequences including sequence reads obtained from anindividual and/or nucleotide sequences derived from the initial sequenceread from a sample obtained from an individual. For example, a readsegment can refer to an aligned sequence read, a collapsed sequenceread, or a stitched read. Furthermore, a read segment can refer to anindividual nucleotide base, such as a single nucleotide variant.

As used herein, the term, “reference exome” refers to any particularknown, sequenced or characterized exome, whether partial or complete, ofany tissue from any organism or pathogen that may be used to referenceidentified sequences from a subject. Example reference exomes used forhuman subjects as well as many other organisms are provided in theon-line genome browser hosted by the National Center for BiotechnologyInformation (“NCBI”).

As used herein, the term “reference genome” refers to any particularknown, sequenced or characterized genome, whether partial or complete,of any organism or pathogen that may be used to reference identifiedsequences from a subject. Exemplary reference genomes used for humansubjects as well as many other organisms are provided in the on-linegenome browser hosted by the National Center for BiotechnologyInformation (“NCBI”) or the University of California, Santa Cruz (UCSC).A “genome” refers to the complete genetic information of an organism orpathogen, expressed in nucleic acid sequences. As used herein, areference sequence or reference genome often is an assembled orpartially assembled genomic sequence from an individual or multipleindividuals. In some embodiments, a reference genome is an assembled orpartially assembled genomic sequence from one or more human individuals.The reference genome can be viewed as a representative example of aspecies' set of genes. In some embodiments, a reference genome comprisessequences assigned to chromosomes. Exemplary human reference genomesinclude but are not limited to NCBI build 34 (UCSC equivalent: hg16),NCBI build 35 (UCSC equivalent: hg17), NCBI build 36.1 (UCSC equivalent:hg18), GRCh37 (UCSC equivalent: hg19), and GRCh38 (UCSC equivalent:hg38).

As used herein, the term “assay” refers to a technique for determining aproperty of a substance, e.g., a nucleic acid, a protein, a cell, atissue, or an organ. An assay (e.g., a first assay or a second assay)can comprise a technique for determining the copy number variation ofnucleic acids in a sample, the methylation status of nucleic acids in asample, the fragment size distribution of nucleic acids in a sample, themutational status of nucleic acids in a sample, or the fragmentationpattern of nucleic acids in a sample. Any assay known to a person havingordinary skill in the art can be used to detect any of the properties ofnucleic acids mentioned herein. Properties of a nucleic acids caninclude a sequence, genomic identity, copy number, methylation state atone or more nucleotide positions, size of the nucleic acid, presence orabsence of a mutation in the nucleic acid at one or more nucleotidepositions, and pattern of fragmentation of a nucleic acid (e.g., thenucleotide position(s) at which a nucleic acid fragments). An assay ormethod can have a particular sensitivity and/or specificity, and theirrelative usefulness as a diagnostic tool can be measured using ROC-AUCstatistics.

The term “classification” can refer to any number(s) or othercharacters(s) that are associated with a particular property of asample. For example, in some embodiments, the term “classification” canrefer to a type of cancer in a subject or sample, a stage of cancer in asubject or sample, a prognosis for a cancer in a subject or sample, atumor load in a subject, a presence of tumor metastasis in a subject,and the like. The classification can be binary (e.g., positive ornegative) or have more levels of classification (e.g., a scale from 1 to10 or 0 to 1). The terms “cutoff” and “threshold” can refer topredetermined numbers used in an operation. For example, a cutoff sizecan refer to a size above which fragments are excluded. A thresholdvalue can be a value above or below which a particular classificationapplies. Either of these terms can be used in either of these contexts.

Several aspects are described below with reference to exampleapplications for illustration. It should be understood that numerousspecific details, relationships, and methods are set forth to provide afull understanding of the features described herein. One having ordinaryskill in the relevant art, however, will readily recognize that thefeatures described herein can be practiced without one or more of thespecific details or with other methods. The features described hereinare not limited by the illustrated ordering of acts or events, as someacts can occur in different orders and/or concurrently with other actsor events. Furthermore, not all illustrated acts or events are requiredto implement a methodology in accordance with the features describedherein.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

B. Example System Embodiments

Now that an overview of some aspects of the present disclosure and somedefinitions used in the present disclosure have been provided, detailsof an exemplary system are now described in conjunction with FIG. 1.FIG. 1 is a block diagram illustrating a system 100 in accordance withsome implementations. The device 100 in some implementations includesone or more processing units CPU(s) 102 (also referred to asprocessors), one or more network interfaces 104, a user interface 106, anon-persistent memory 111, a persistent memory 112, and one or morecommunication buses 114 for interconnecting these components. The one ormore communication buses 114 optionally include circuitry (sometimescalled a chipset) that interconnects and controls communications betweensystem components. The non-persistent memory 111 typically includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM, ROM,EEPROM, flash memory, whereas the persistent memory 112 typicallyincludes CD-ROM, digital versatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. The persistent memory 112 optionally includes one ormore storage devices remotely located from the CPU(s) 102. Thepersistent memory 112, and the non-volatile memory device(s) within thenon-persistent memory 112, comprise non-transitory computer readablestorage medium. In some implementations, the non-persistent memory 111or alternatively the non-transitory computer readable storage mediumstores the following programs, modules and data structures, or a subsetthereof, sometimes in conjunction with the persistent memory 112:

-   -   an optional operating system 116, which includes procedures for        handling various basic system services and for performing        hardware dependent tasks;    -   an optional network communication module (or instructions) 118        for connecting the system 100 with other devices and/or a        communication network;    -   a test subject data store 120 for storing datasets containing        biological information and/or personal characteristics about        test subjects, including sequencing data 122, nucleic acid        features 124, personal characteristics 126, and/or organoid        sensitivities 128;    -   an optional classifier training module 130 for training        classifiers, e.g., to distinguish between disease states or        predicted treatment outcomes, for example, based on information        learned from studies of patient-based organoid cultures;    -   an optional disease classification module 130 for classifying        the cancer status of a subject based on test subject data, e.g.,        sequencing data 122, nucleic acid features 124, personal        characteristics 126, and/or patient-derived organoid        sensitivities 128 stored in test subject data store 120;    -   an optional drug classification module 150 for classifying a        utility of a therapeutic compound based on organoid-derived        sensitivities to the therapeutic compound; and    -   an optional patient reporting module 160 for generating reports        about the disease (e.g., cancer) status of a test subject.

In various implementations, one or more of the above identified elementsare stored in one or more of the previously mentioned memory devices,and correspond to a set of instructions for performing a functiondescribed above. The above identified modules, data, or programs (e.g.,sets of instructions) need not be implemented as separate softwareprograms, procedures, datasets, or modules, and thus various subsets ofthese modules and data may be combined or otherwise re-arranged invarious implementations.

In some implementations, the non-persistent memory 111 optionally storesa subset of the modules and data structures identified above.Furthermore, in some embodiments, the memory stores additional modulesand data structures not described above. In some embodiments, one ormore of the above identified elements is stored in a computer system,other than that of system 100, that is addressable by system 100 so thatsystem 100 may retrieve all or a portion of such data when needed.

Although FIG. 1 depicts a “system 100,” the figure is intended more asfunctional description of the various features which may be present inone or more computer systems than as a structural schematic of theimplementations described herein. In practice, and as recognized bythose of ordinary skill in the art, items shown separately could becombined and some items could be separated. Moreover, although FIG. 1depicts certain data and modules in non-persistent memory 111, some orall of these data and modules may be in persistent memory 112.

For instance, as depicted in FIG. 2, in some embodiments the methodsdescribed herein are performed across a distributed diagnosticenvironment 210, e.g., connected via communication network 212. In someembodiments, one or more biological sample, e.g., one or more tumorbiopsy or control sample, is collected from a subject in clinicalenvironment 220, e.g., a doctor's office, hospital, or medical clinic.In some embodiments, a portion of the sample is processed within theclinical environment using a processing device 224, e.g., a nucleic acidsequencer for obtaining sequencing data, a microscope for obtainingpathology data, a mass spectrometer for obtaining proteomic data, etc.In some embodiments, the biological sample or a portion of thebiological sample is sent to one or more external environments, e.g.,sequencing lab 230, pathology lab 240, and molecular biology lab 250,each of which includes a processing device 234, 244, and 254,respectively, to generate biological data about the subject. Eachenvironment includes a communications device 222, 232, 242, and 252,respectively, for communicating biological data about the subject to aprocessing server 262 and/or database 264, which may located in yetanother environment, e.g., processing/storage center 260. Thus, in someembodiments, different portions of the systems and methods describedherein are fulfilled by different processing devices located indifferent physical environments.

C. Classifiers

In some embodiments, the methods described herein use biologicalfeatures, e.g., genomic features, of a subject and/or cultured organoid,e.g., a patient-specific tumor organoid, to classify a condition, e.g.,cancer, predicted an effect of a particular therapy, etc. Generally, anyclassifier architecture can be trained for these purposes. Non-limitingexamples of classifier types that can be used in conjunction with themethods described herein include a machine learning algorithm, a neuralnetwork algorithm, a support vector machine algorithm, a decision treealgorithm, an unsupervised clustering model algorithm, a supervisedclustering model algorithm, or a regression model. In some embodiments,the trained classifier is binomial or multinomial.

In some embodiments, the classifier is implemented as an artificialintelligence engine and may include gradient boosting models, randomforest models, neural networks (NN), regression models, Naive Bayesmodels, and/or machine learning algorithms (MLA). MLAs includesupervised algorithms (such as algorithms where thefeatures/classifications in the data set are annotated) using linearregression, logistic regression, decision trees, classification andregression trees, naïve Bayes, nearest neighbor clustering; unsupervisedalgorithms (such as algorithms where no features/classification in thedata set are annotated) using apriori, means clustering, principalcomponent analysis, random forest, adaptive boosting; andsemi-supervised algorithms (such as algorithms where an incompletenumber of features/classifications in the data set are annotated) usinggenerative approach (such as a mixture of Gaussian distributions,mixture of multinomial distributions, hidden Markov models), low densityseparation, graph-based approaches (such as mincut, harmonic function,manifold regularization), heuristic approaches, or support vectormachines.

While MLA and neural networks identify distinct approaches to machinelearning, the terms may be used interchangeably herein. Thus, a mentionof MLA may include a corresponding NN or a mention of NN may include acorresponding MLA unless explicitly stated otherwise. Training mayinclude providing optimized datasets, labeling these traits as theyoccur in patient records, and training the MLA to predict or classifybased on new inputs. Artificial NNs are efficient computing models whichhave shown their strengths in solving hard problems in artificialintelligence. They have also been shown to be universal approximators,that is, they can represent a wide variety of functions when givenappropriate parameters.

Neural network (NN) algorithms, including convolutional neural networkalgorithms, that can serve as the classifier for the instant methods aredisclosed in See, Vincent et al., 2010, “Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoisingcriterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009,“Exploring strategies for training deep neural networks,” J Mach LearnRes 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial NeuralNetworks, Massachusetts Institute of Technology, each of which is herebyincorporated by reference. NNs include conditional random fields,convolutional neural networks, attention based neural networks, deeplearning, long short term memory networks, or other neural models.

Example logistic regression algorithms are disclosed in Agresti, AnIntroduction to Categorical Data Analysis, 1996, Chapter 5, pp. 103-144,John Wiley & Son, New York, which is hereby incorporated by reference.

Support vector machine (SVM) algorithms that can serve as the classifierfor the instant methods are described in Cristianini and Shawe-Taylor,2000, “An Introduction to Support Vector Machines,” Cambridge UniversityPress, Cambridge; Boser et al., 1992, “A training algorithm for optimalmargin classifiers,” in Proceedings of the 5^(th) Annual ACM Workshop onComputational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152;Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001,Bioinformatics: sequence and genome analysis, Cold Spring HarborLaboratory Press, Cold Spring Harbor, N.Y.; Duda, PatternClassification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259,262-265; and Hastie, 2001, The Elements of Statistical Learning,Springer, New York; and Furey et al., 2000, Bioinformatics 16, 906-914,each of which is hereby incorporated by reference in its entirety. Whenused for classification, SVMs separate a given set of binary-labeleddata training set with a hyper-plane that is maximally distant from thelabeled data. For cases in which no linear separation is possible, SVMscan work in combination with the technique of ‘kernels’, whichautomatically realizes a non-linear mapping to a feature space. Thehyper-plane found by the SVM in feature space corresponds to anon-linear decision boundary in the input space. Decision trees (e.g.,random forest, boosted trees) that can serve as the classifier for theinstant methods are described generally by Duda, 2001, PatternClassification, John Wiley & Sons, Inc., New York, pp. 395-396, which ishereby incorporated by reference. Tree-based methods partition thefeature space into a set of rectangles, and then fit a model (like aconstant) in each one. In some embodiments, the decision tree is randomforest regression. One specific algorithm that can serve as theclassifier for the instant methods is a classification and regressiontree (CART). Other specific decision tree algorithms that can serve asthe classifier for the instant methods include, but are not limited to,ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are describedin Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., NewYork, pp. 396-408 and pp. 411-412, which is hereby incorporated byreference. CART, MART, and C4.5 are described in Hastie et al., 2001,The Elements of Statistical Learning, Springer-Verlag, New York, Chapter9, which is hereby incorporated by reference in its entirety. RandomForests are described in Breiman, 1999, “Random Forests—RandomFeatures,” Technical Report 567, Statistics Department, U. C. Berkeley,September 1999, which is hereby incorporated by reference in itsentirety.

D. Biological Samples

In some embodiments, one or more biological samples collected from asubject is a solid tissue sample, e.g., a solid tumor sample or a solidnormal tissue sample. Methods for obtaining solid tissue samples, e.g.,of cancerous and/or normal tissue are known in the art, and aredependent upon the type of tissue being sampled. For example, bonemarrow biopsies and isolation of circulating tumor cells can be used toobtain samples of blood cancers, endoscopic biopsies can be used toobtain samples of cancers of the digestive tract, bladder, and lungs,needle biopsies (e.g., fine-needle aspiration, core needle aspiration,vacuum-assisted biopsy, and image-guided biopsy, can be used to obtainsamples of subdermal tumors, skin biopsies, e.g., shave biopsy, punchbiopsy, incisional biopsy, and excisional biopsy, can be used to obtainsamples of dermal cancers, and surgical biopsies can be used to obtainsamples of cancers affecting internal organs of a patient. In someembodiments, a solid tissue sample is a formalin-fixed tissue (FFT). Insome embodiments, a solid tissue sample is a macro-dissected formalinfixed paraffin embedded (FFPE) tissue. In some embodiments, a solidtissue sample is a fresh frozen tissue sample.

In some embodiments, one or more of the biological samples collectedfrom a subject is a liquid biological sample, also referred to as aliquid biopsy sample. In some embodiments, one or more of the biologicalsamples obtained from the patient are selected from blood, plasma,serum, urine, vaginal fluid, fluid from a hydrocele (e.g., of thetestis), vaginal flushing fluids, pleural fluid, ascitic fluid,cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolarlavage fluid, discharge fluid from the nipple, aspiration fluid fromdifferent parts of the body (e.g., thyroid, breast), etc. In someembodiments, the liquid biopsy sample includes blood and/or saliva. Insome embodiments, the liquid biopsy sample is peripheral blood. In someembodiments, blood samples are collected from patients in commercialblood collection containers, e.g., using a PAXgene® Blood DNA Tubes. Insome embodiments, saliva samples are collected from patients incommercial saliva collection containers, e.g., using an Oragene® DNASaliva Kit.

Liquid biopsy samples include cell free nucleic acids, includingcell-free DNA (cfDNA). As described above, cfDNA isolated from cancerpatients includes DNA originating from cancerous cells, also referred toas circulating tumor DNA (ctDNA), cfDNA originating from germline (e.g.,healthy or non-cancerous) cells, and cfDNA originating fromhematopoietic cells (e.g., white blood cells). The relative proportionsof cancerous and non-cancerous cfDNA present in a liquid biopsy samplevaries depending on the characteristics (e.g., the type, stage, lineage,genomic profile, etc.) of the patient's cancer.

cfDNA is a particularly useful source of biological data for variousimplementations of the methods and systems described herein, because itis readily obtained from various body fluids. Advantageously, use ofbodily fluids facilitates serial monitoring because of the ease ofcollection, as these fluids are collectable by non-invasive orminimally-invasive methodologies. This is in contrast to methods thatrely upon solid tissue samples, such as biopsies, which often timesrequire invasive surgical procedures. Further, because bodily fluids,such as blood, circulate throughout the body, the cfDNA populationrepresents a sampling of many different tissue types from many differentlocations.

In some embodiments, a liquid biopsy sample is separated into twodifferent samples. For example in some embodiments, a blood sample isseparated into a blood plasma sample, containing cfDNA, and a buffy coatpreparation, containing white blood cells.

In some embodiments, a dedicated normal sample is also collected from asubject, for co-processing with a solid or liquid cancer sample.Generally, the normal sample is of a non-cancerous tissue, and can becollected using any tissue collection means described above. In someembodiments, buccal cells collected from the inside of a patient'scheeks are used as a normal sample. Buccal cells can be collected byplacing an absorbent material, e.g., a swab, in the subjects mouth andrubbing it against their cheek, e.g., for at least 15 second or for atleast 30 seconds. The swab is then removed from the patient's mouth andinserted into a tube, such that the tip of the tube is submerged into aliquid that serves to extract the buccal cells off of the absorbentmaterial. An example of buccal cell recovery and collection devices isprovided in U.S. Pat. No. 9,138,205, the content of which is herebyincorporated by reference, in its entirety, for all purposes. In someembodiments, the buccal swab DNA is used as a source of normal DNA incirculating heme malignancies.

The biological samples collected from the patient are, optionally, sentto various analytical environments (e.g., sequencing lab 230, pathologylab 240, and/or molecular biology lab 250) for processing (e.g., datacollection) and/or analysis (e.g., feature extraction). Wet labprocessing 204 may include cataloguing samples (e.g., accessioning),examining clinical features of one or more samples (e.g., pathologyreview), and nucleic acid sequence analysis (e.g., extraction, libraryprep, capture+hybridize, pooling, and sequencing). In some embodiments,the workflow includes clinical analysis of one or more biologicalsamples collected from the subject, e.g., at a pathology lab 240 and/ora molecular and cellular biology lab 250, to generate clinical featuressuch as pathology features 128-3, imaging data 128-3, and/or tissueculture/organoid data 128-3.

E. Feature Extraction

In some embodiments, tissue culture/organoid data includes featuresidentified by evaluation of cultured tissue from the subject. Forinstance, in some embodiments, tissue samples obtained from the patients(e.g., tumor tissue, normal tissue, or both) are cultured (e.g., inliquid culture, solid-phase culture, and/or organoid culture) andvarious features, such as cell morphology, growth characteristics,genomic alterations, and/or drug sensitivity, are evaluated. In someembodiments, tissue culture/organoid data 128-3 includes featuresdetermined using machine learning algorithms to evaluate tissueculture/organoid data collected as described herein. Genetic sequencingused to generate the features used in the methods described herein maybe done on either a tumor organoid, e.g., cultured from cells of a testsubject, or the source tissue (patient biopsy used to generate the TO),e.g., to determine the tumor organoid's (e.g., the subject's cancer)BRCA1/2 mutation status, loss of heterozygosity, bi-allelic loss status,etc.

In some embodiments of the methods described herein, nucleic acidsequencing of one or more samples, e.g., a biological sample from asubject and/or a tissue culture/organoid sample, is performed. Briefly,nucleic acids, e.g., RNA and/or DNA are extracted (304) from the one ormore biological samples. Methods for isolating nucleic acids frombiological samples are known in the art, and are dependent upon the typeof nucleic acid being isolated (e.g., cfDNA, DNA, and/or RNA) and thetype of sample from which the nucleic acids are being isolated (e.g.,liquid biopsy samples, white blood cell buffy coat preparations,formalin-fixed paraffin-embedded (FFPE) solid tissue samples, and freshfrozen solid tissue samples). The selection of any particular nucleicacid isolation technique for use in conjunction with the embodimentsdescribed herein is well within the skill of the person having ordinaryskill in the art, who will consider the sample type, the state of thesample, the type of nucleic acid being sequenced and the sequencingtechnology being used.

For instance, many techniques for DNA isolation, e.g., genomic DNAisolation, from a tissue sample are known in the art, such as organicextraction, silica adsorption, and anion exchange chromatography.Likewise, many techniques for RNA isolation, e.g., mRNA isolation, froma tissue sample are known in the art. For example, acid guanidiniumthiocyanate-phenol-chloroform extraction (see, for example, Chomczynskiand Sacchi, 2006, Nat Protoc, 1(2):581-85, which is hereby incorporatedby reference herein), and silica bead/glass fiber adsorption (see, forexample, Poeckh, T. et al., 2008, Anal Biochem., 373(2):253-62, which ishereby incorporated by reference herein). The selection of anyparticular DNA or RNA isolation technique for use in conjunction withthe embodiments described herein is well within the skill of the personhaving ordinary skill in the art, who will consider the tissue type, thestate of the tissue, e.g., fresh, frozen, formalin-fixed,paraffin-embedded (FFPE), and the type of nucleic acid analysis that isto be performed.

In some embodiments, a nucleic acid library is prepared from theisolated nucleic acids (e.g., cfDNA, DNA, and/or RNA). For example, insome embodiments, DNA libraries (e.g., gDNA and/or cfDNA libraries) areprepared from isolated DNA from the one or more biological samples. Insome embodiments, the DNA libraries are prepared using a commerciallibrary preparation kit, e.g., the KAPA Hyper Prep Kit, a New EnglandBiolabs (NEB) kit, or a similar kit.

In some embodiments, during library preparation, adapters (e.g., UDIadapters, such as Roche SeqCap dual end adapters, or UMI adapters suchas full length or stubby Y adapters) are ligated onto the nucleic acidmolecules. In some embodiments, the adapters include unique molecularidentifiers (UMIs), which are short nucleic acid sequences (e.g., 3-10base pairs) that are added to ends of DNA fragments during adapterligation. In some embodiments, UMIs are degenerate base pairs that serveas a unique tag that can be used to identify sequence reads originatingfrom a specific DNA fragment. In some embodiments, e.g., when multiplexsequencing will be used to sequence DNA from a plurality of samples(e.g., from the same or different subjects) in a single sequencingreaction, a patient-specific index is also added to the nucleic acidmolecules. In some embodiments, the patient specific index is a shortnucleic acid sequence (e.g., 3-20 nucleotides) that are added to ends ofDNA fragments during library construction, that serve as a unique tagthat can be used to identify sequence reads originating from a specificpatient sample. Examples of identifier sequences are described, forexample, in Kivioja et al., Nat. Methods 9(1):72-74 (2011) and Islam etal., Nat. Methods 11(2):163-66 (2014), the contents of which are herebyincorporated by reference, in their entireties, for all purposes.

In some embodiments, an adapter includes a PCR primer landing site,designed for efficient binding of a PCR or second-strand synthesisprimer used during the sequencing reaction. In some embodiments, anadapter includes an anchor binding site, to facilitate binding of theDNA molecule to anchor oligonucleotide molecules on a sequencer flowcell, serving as a seed for the sequencing process by providing astarting point for the sequencing reaction. During PCR amplificationfollowing adapter ligation, the UMIs, patient indexes, and binding sitesare replicated along with the attached DNA fragment. This provides a wayto identify sequence reads that came from the same original fragment indownstream analysis.

In some embodiments, a sequencing library, or pool of sequencinglibraries, is enriched for target nucleic acids, e.g., nucleic acidsencompassing loci that are informative for precision oncology and/orused as internal controls for sequencing or bioinformatics processes. Insome embodiments, enrichment is achieved by hybridizing target nucleicacids in the sequencing library to probes that hybridize to the targetsequences, and then isolating the captured nucleic acids away fromoff-target nucleic acids that are not bound by the capture probes.

In some embodiments, e.g., where a whole genome sequencing method willbe used, nucleic acid sequencing libraries are not target-enriched priorto sequencing, in order to obtain sequencing data on substantially allof the competent nucleic acids in the sequencing library. Similarly, insome embodiments, e.g., where a whole genome sequencing method will beused, nucleic acid sequencing libraries are not mixed, because ofbandwidth limitations related to obtaining significant sequencing depthacross an entire genome. However, in other embodiments, e.g., where alow pass whole genome sequencing (LPWGS) methodology will be used,nucleic acid sequencing libraries can still be pooled, because very lowaverage sequencing coverage is achieved across a respective genome,e.g., between about 0.5× and about 5×.

In some embodiments, the probe set includes probes targeting one or moregene loci, e.g., exon or intron loci. In some embodiments, the probe setincludes probes targeting one or more loci not encoding a protein, e.g.,regulatory loci, miRNA loci, and other non-coding loci, e.g., that havebeen found to be associated with cancer. In some embodiments, theplurality of loci include at least 25, 50, 100, 150, 200, 250, 300, 350,400, 500, 750, 1000, 2500, 5000, or more human genomic loci. In someembodiments, the gene panel is a whole-exome panel that analyzes theexomes of a biological sample. In some embodiments, the gene panel is awhole-genome panel that analyzes the genome of a specimen.

Sequence reads are then generated from the sequencing library or pool ofsequencing libraries. Sequencing data may be acquired by any methodologyknown in the art. For example, next generation sequencing (NGS)techniques such as sequencing-by-synthesis technology (Illumina),pyrosequencing (454 Life Sciences), ion semiconductor technology (IonTorrent sequencing), single-molecule real-time sequencing (PacificBiosciences), sequencing by ligation (SOLiD sequencing), nanoporesequencing (Oxford Nanopore Technologies), or paired-end sequencing. Insome embodiments, massively parallel sequencing is performed usingsequencing-by-synthesis with reversible dye terminators. In someembodiments, sequencing is performed using next generation sequencingtechnologies, such as short-read technologies. In other embodiments,long-read sequencing or another sequencing method known in the art isused.

In some embodiments, the methods described herein include one or more ofobtaining a biological sample, extracting nucleic acids from thebiological sample, and sequencing the isolated nucleic acids. In otherembodiments, sequencing data or feature data sets obtained therefromused in the improved systems and methods described herein are obtainedby receiving previously generated sequence reads or feature data sets,in electronic form.

In some embodiments, the methods and systems described herein useadditional features to perform the various classifications providedherein. Non-limiting examples of other characteristics that may be usedfor these purposes include tumor organoid histology, and baselinehistology (H&E image; features; see also Patent Application Nos.62/787,047 and 62/824,039); clinical data (gender, age, family medicalhistory, personal history of cancer, treatment history of cancer,diagnosis, subtype, IHC markers, DNA/RNA sequence); methylationsequence; radiology images/features; comorbidities, geographic location,ancestry, ethnic features, diet, gut microbiome, substance usage,alcohol use, physical exam findings, baseline vitals, vaccinationhistory, religious or cultural beliefs, marital status, sexualorientation, nocturnal/diurnal rhythm, sleep schedule, travel history(including airplane travel), travel to certain regions, presence/absenceof parasitic organisms, infectious disease history (including parasiticorganisms, viruses, bacteria), germline DNA sequence, biometric features(like distance between eyes, bones), skull shape, physical profile,occupational history, living with pets (including birds), mental health;tumor organoid morphology; viability; and smoking status.

F. Training a Classifier to Evaluate Sensitivity to a Therapeutic Agent

In one aspect, the disclosure provides methods and systems forperforming methods of training a classifier to discriminate between twoor more tumor sensitivities to a therapeutic agent. In some embodiments,the method includes obtaining a data set comprising, for each respectivetissue sample in a plurality of tissue samples, (i) a correspondingplurality of nucleic acid features of the tissue sample, and (ii) acorresponding indication of the sensitivity of a respective organoidcultured from one or more cells of the tissue sample to the therapeuticagent. The method then includes training an untrained classifier againstat least (i) the corresponding plurality of features and (ii) thecorresponding indication of the sensitivity of the organoid to thetherapeutic agent, across the plurality of tissue samples, therebyobtaining a trained classifier that discriminates between the two ormore tumor sensitivities to a therapeutic agent.

In some embodiments, the two or more tumor sensitivities include a firstlevel of sensitivity to a first therapeutic agent and a second level ofsensitivity to the first therapeutic agent. In some embodiments, the twoor more tumor sensitivities include a first sensitivity to a firsttherapeutic agent and a second sensitivity to a second therapeuticagent.

In some embodiments, the classifier is further trained to discriminatebetween the two or more tumor sensitivities to a therapeutic agent andone or more tumor sensitivity to a chemotherapeutic drug that is not thetherapeutic agent.

In some embodiments, each tissue sample in the plurality of tissuesamples was obtained from a subject that had not received treatment forcancer prior to the tissue sample being obtained. In some embodiments,each tissue sample in the plurality of tissue samples is a breast cancerbiopsy. In some embodiments, each tissue sample in the plurality oftissue samples is an ovarian cancer biopsy. In some embodiments, eachtissue sample in the plurality of tissue samples is a biopsy of ahomologous recombination deficient (HRD) cancer. In some embodiments,each tissue sample in the plurality of tissue samples is a biopsy from acancer other than breast cancer or ovarian cancer.

In some embodiments, the plurality of nucleic acid features of thetissue sample includes one or more of support for a single nucleotidevariant at a genomic location, a methylation status at a genomiclocation, a relative copy number for a genomic location, an allelicratio for a genomic location, a relative expression level of a gene, andmathematical combinations thereof. In some embodiments, at least asub-plurality of the plurality of nucleic acid features of the tissuesample are measured from a tumor organoid cultured from one or morecells of the tissue sample.

In some embodiments, the indication of the sensitivity of the respectiveorganoid to the therapeutic agent is based at least in part on one ormore cellular fitness measurements obtained by: i) culturing one or moreorganoids, each respective organoid in the one or more organoids fromone or more cells of the respective tissue sample, ii) exposing the oneor more organoids cultured in i) to one or more amounts of thetherapeutic agent, and iii) measuring the fitness of cells, e.g., usinga cellular viability assay or cell death assay, in the one or moreorganoids following the exposure to the one or more amounts of thetherapeutic agent, thereby obtaining the one or more cellular fitnessmeasurements.

In some embodiments, the one or more cellular fitness measurements areone or more measurements of cellular apoptosis following exposure to thetherapeutic agent.

In some embodiments, for at least one respective tissue sample in theplurality of tissue samples, the one or more cultured organoids areexposed to a sensitizing therapy, e.g., radiation or a differentchemotherapeutic drug, before being exposed to the therapeutic agent.

In some embodiments, the untrained classifier is further trainedagainst, for each respective tissue sample in the plurality of tissuesamples, a corresponding cancer classification of the tissue sample in aplurality of cancer classifications. In some embodiments, the pluralityof cancer classifications includes cancerous tissue and non-canceroustissue. In some embodiments, the plurality of cancer classificationsincludes a plurality of types of cancer. In some embodiments, theplurality of cancer classifications includes a plurality of stages ofcancer.

In some embodiments, the plurality of tissue samples includes aplurality of tumor biopsies, and the classifier training includesidentifying a plurality of genes that are differentially expressedbetween (i) organoids, cultured from one or more cells of a respectivetumor biopsy in the plurality of tumor biopsies, that have a firstsensitivity to the therapeutic agent, e.g., organoids that experience atleast a threshold level of cell death upon exposure of the organoid tothe therapeutic agent, and (ii) organoids, cultured from one or morecells of a respective tumor biopsy in the plurality of tumor biopsies,that have a second sensitivity to the therapeutic agent, e.g., organoidsthat experience less than a threshold level of cell death upon exposureof the organoid to the therapeutic agent.

In some embodiments, the classifier is further trained against, for eachrespective tissue sample in the plurality of tissue samples, one or morephenotypic characteristics of the tissue sample. In some embodiments,the one or more phenotypic characteristics of the tissue sample includea histologic feature of the tissue sample.

In some embodiments, the classifier is further trained against, for eachrespective tissue sample in the plurality of tissue samples, one or morecharacteristics of the subject from which the tissue sample wasobtained, e.g., as described above.

In some embodiments, the classifier is a multinomial classifier that istrained to provide a plurality of likelihoods, wherein each respectivelikelihood in the plurality of likelihoods is a likelihood that tumorcells from a test subject with cancer will have a different respectivesensitivity in the two or more tumor sensitivities to a therapeuticagent. In some embodiments, the classifier is a neural networkalgorithm, a support vector machine algorithm, a Naive Bayes algorithm,a nearest neighbor algorithm, a boosted trees algorithm, a random forestalgorithm, a convolutional neural network algorithm, a decision treealgorithm, a regression algorithm, or a clustering algorithm.

G. Diagnosing Therapeutic Agent Sensitivity

In some embodiments, the disclosure provides method and systems forperforming methods of recommending therapy for treating a cancer in asubject. In some embodiments, the method includes obtaining a first testdata set comprising a plurality of nucleic acid features of a tumorbiopsy from the test subject. The method then includes evaluating thefirst test data set using a classifier trained to discriminate betweentwo or more tumor sensitivities to a first therapeutic agent. Theclassifier is trained against, for each respective training tissuesample in a plurality of training tissue samples, at least (i) theplurality of nucleic acid features obtained from the respective trainingtissue sample, and (ii) a corresponding indication of the sensitivity ofa respective organoid cultured from one or more cells of the respectivetraining tissue sample to the first therapeutic agent. In someembodiments, the sensitivity of the training tissue sample is determinedin a two-dimensional cell culture, a three-dimensional cell cultureprepared in a matrix, a three-dimensional cell culture prepared insuspension culture, a cell culture suspension, or a xenograft model. Afirst tumor sensitivity in the two or more tumor sensitivities to thefirst therapeutic agent is associated with a first recommendation, in aplurality of recommendations, to treat the cancer in the subject withthe first therapeutic agent. A second tumor sensitivity in the two ormore tumor sensitivities to the first therapeutic agent is associatedwith a second recommendation, in the plurality of recommendations, notto treat the cancer in the subject with the first therapeutic agent. Insome embodiments, the method includes providing a respectiverecommendation, in the plurality of recommendations, for treating thecancer in the test subject based on the results of the evaluation.

In some embodiments, the cancer is a breast cancer. In some embodiments,the cancer is an ovarian cancer. In some embodiments, the cancer is notbreast cancer or ovarian cancer. In some embodiments, the test subjecthas not previously been treated for cancer. That is, in someembodiments, the therapeutic agent is administered as a first-linetherapy. In some embodiments, the test subject has been previouslytreated for the cancer. That is, in some embodiments, the therapeuticagent is administered as a second-line therapy.

In some embodiments, the plurality of nucleic acid features of the tumorbiopsy comprise one or more of support for a single nucleotide variantat a genomic location, a methylation status at a genomic location, arelative copy number for a genomic location, an allelic ratio for agenomic location, a relative expression level of a gene, andmathematical combinations thereof.

In some embodiments, the first test data set further comprises a cancerstatus of the test subject in a plurality of cancer statuses, theclassifier was further trained against, for each respective trainingtissue sample in a plurality of training tissue samples, a cancer statusof the respective training tissue sample in the plurality of cancerstatuses.

In some embodiments, the plurality of cancer statuses comprises aplurality of types of cancer. In some embodiments, the plurality ofcancer statuses comprises a plurality of stages of cancer.

In some embodiments, the first test data set further comprises one ormore phenotypic characteristics of the tumor biopsy, and the classifierwas further trained against, for each respective training tissue samplein a plurality of training tissue samples, the one or more phenotypiccharacteristics of the respective training tissue sample. In someembodiments, the one or more phenotypic characteristics of the tumorbiopsy comprise a histologic feature of the tumor biopsy.

In some embodiments, the first test data set further comprises one ormore characteristics of the subject, as described in the featureselection section above, and the classifier was further trained against,for each respective training tissue sample in a plurality of trainingtissue samples, the one or more characteristics of the subject fromwhich the respective training tissue sample was obtained.

In some embodiments, the method also includes obtaining a second testdata set comprising the plurality of nucleic acid features of a tumorbiopsy from the test subject, and evaluating the second test data setusing the classifier trained to discriminate between two or more tumorsensitivities to a first therapeutic agent, where the plurality ofnucleic acid features in the first test data set are from a first tumorbiopsy from the test subject and the second test data set are from asecond tumor biopsy from the test subject that is different from thefirst tumor biopsy. In some embodiments, the first tumor biopsy and thesecond tumor biopsy are from different sections of a same tumor of thesubject. In some embodiments, the first tumor biopsy and the secondtumor biopsy are from different tumors of the subject.

In some embodiments, the classifier is a multinomial classifier that istrained to provide a plurality of likelihoods, wherein each respectivelikelihood in the plurality of likelihoods is a likelihood that adifferent respective treatment regime, in a plurality of treatmentregimes, for the cancer in the subject will be more effective than theother treatment regimes in the plurality of treatment regimes.

In some embodiments, the classifier is a neural network algorithm, asupport vector machine algorithm, a Naive Bayes algorithm, a nearestneighbor algorithm, a boosted trees algorithm, a random forestalgorithm, a convolutional neural network algorithm, a decision treealgorithm, a regression algorithm, or a clustering algorithm.

In some embodiments, the classifier is trained to further discriminatebetween two or more tumor sensitivities to a second therapeutic agent,wherein the second therapeutic agent is different than the firsttherapeutic agent In some embodiments, the classifier is trained tofurther discriminate between two or more tumor sensitivities to achemotherapeutic drug that is not in the same class of agents as thetherapeutic agent.

In some embodiments, the first recommendation is a recommendation totreat the cancer in the subject with the first therapeutic agent as afirst-line therapy. In some embodiments, the first recommendation is arecommendation to treat the cancer in the subject with the firsttherapeutic agent as a second-line therapy.

In some embodiments, the first recommendation is a recommendation totreat the cancer in the subject with the first therapeutic agent andwithout any other chemotherapeutic drug. In some embodiments, afterproviding, or instead of providing, the first recommendation fortreating the cancer in the subject, the method includes administeringthe first therapeutic agent to the test subject.

In some embodiments, the first recommendation is a recommendation totreat the cancer in the subject with the first therapeutic agent andwith a second cancer therapy, e.g., radiation, surgery, a secondtherapeutic agent, or a chemotherapeutic drug that is not in the sameclass as first therapeutic agent. In some embodiments, after providing,or instead of providing, the first recommendation for treating thecancer in the subject, the method includes co-administering the firsttherapeutic agent and the second cancer therapy to the test subject. Insome embodiments, the second cancer therapy is administration of achemotherapeutic drug that is not in the same class as the firsttherapeutic agent.

In some embodiments, the second recommendation is a recommendation totreat the cancer in the subject with a second therapeutic agent. In someembodiments, after providing, or instead of providing, the secondrecommendation for treating the cancer in the subject, the methodincludes administering the second therapeutic agent to the test subject.

In some embodiments, the plurality of recommendations further comprisesa third recommendation to treat the cancer in the subject with achemotherapeutic drug that is not in the same class as the first orsecond therapeutic agent. In some embodiments, after providing, orinstead of providing, the third recommendation for treating the cancerin the subject, the method includes administering the chemotherapeuticdrug that is not in the same class as the first or second therapeuticagent to the test subject.

In some embodiments, prior to providing the respective recommendation,the method includes identifying at least two recommendations, in theplurality of recommendations, based on the results of the evaluation,e.g., that are associated with better likelihoods that their associatedtreatment regimens will be more effective than treatment regimensassociated with other recommendations, and determining a sensitivity ofthe cancer in the subject to each respective therapy regimen, in aplurality of therapy regimens, associated with the at least tworecommendations by: a) culturing a plurality of tumor organoids, eachrespective tumor organoid in the plurality of tumor organoids from oneor more cells of a tumor biopsy from the subject, b) exposing the one ormore organoids cultured in a) to one or more amounts of a therapeuticagent associated with the respective therapy regimen, and c) measuringthe fitness of cells, e.g., using a cellular viability assay or celldeath assay, in the one or more organoids following the exposure to theone or more amounts of the therapeutic agent, thereby obtaining one ormore cellular fitness measurements for each respective therapy regimenassociated with the two or more recommendations. In this embodiments,the therapeutic recommendation and/or treatment is selected from the twoor more recommendations and is based on the one or more cellular fitnessmeasurements obtained for each respective therapy regimen associatedwith the two or more recommendations.

In some embodiments, the methods described herein include administeringthe recommended therapy for treating cancer to the test subject.

H. Treatment of Cancers with Therapeutic Agents

In some embodiments, the disclosure provides methods and systems forperforming methods of treating a cancer in a test subject. In someembodiments, the methods include determining a probability or likelihoodthat the cancer will be sensitive to a therapeutic agent, and treatingcancer in the subject. When the probability or likelihood that thecancer will be sensitive to the therapeutic agent satisfies a thresholdlikelihood, the method includes administering, or communicating, arecommended therapy comprising the therapeutic agent to the testsubject. When the probability or likelihood that the cancer will besensitive to the therapeutic agent does not satisfy a thresholdlikelihood, the method includes administrating, or communicating, arecommended therapy that does not include the therapeutic agent to thetest subject.

In some embodiments, the cancer is a basal cell skin cancer, a squamouscancer, a breast cancer, a bladder cancer, a cervical cancer, a coloncancer, an endometrial cancer, a head and neck cancer, a hepatobiliarycancer, a kidney cancer, a gastric cancer, a lung cancer, a mesothelialcancer of the pleural cavity, a mesothelial cancer of the peritonealcavity, an ovarian cancer, prostate cancer, or a rectal cancer. In someembodiments, the test subject has been previously treated for thecancer. That is, in some embodiments, the therapeutic agent isadministered as a second-line therapy.

In some embodiments, the probability or likelihood that the cancer willbe sensitive to the therapeutic agent is determined by: 1) culturing aplurality of tumor organoids, each respective tumor organoid in theplurality of tumor organoids from one or more cells of a tumor biopsyfrom the test subject, 2) exposing the one or more organoids cultured ina) to one or more amounts of the therapeutic agent, 3) measuring thefitness of cells, e.g., using a cellular viability assay or cell deathassay, in the one or more tumor organoids following the exposure to theone or more amounts of the therapeutic agent, and 4) correlating themeasured fitness of the cells with a probability or likelihood that thecancer will be sensitive to the therapeutic agent.

In some embodiments, the probability or likelihood that the cancer willbe sensitive to the therapeutic agent is determined by: 1) obtaining afirst test data set comprising a plurality of nucleic acid features of atumor biopsy from the test subject, 2) evaluating the first test dataset using a classifier trained to discriminate between two or more tumorsensitivities to the therapeutic agent, where the classifier was trainedagainst, for each respective training tissue sample in a plurality oftraining tissue samples, at least (i) the plurality of nucleic acidfeatures obtained from the respective training tissue sample, and (ii) acorresponding indication of the sensitivity of a respective organoidcultured from one or more cells of the respective training tissue sampleto the therapeutic agent.

In some embodiments, the plurality of nucleic acid features of the tumorbiopsy comprise one or more of support for a single nucleotide variantat a genomic location, a methylation status at a genomic location, arelative copy number for a genomic location, an allelic ratio for agenomic location, a relative expression level of a gene, andmathematical combinations thereof.

In some embodiments, the first test data set further comprises a cancerstatus of the test subject in a plurality of cancer statuses, and theclassifier was further trained against, for each respective trainingtissue sample in a plurality of training tissue samples, a cancer statusof the respective training tissue sample in the plurality of cancerstatuses. In some embodiments, the plurality of cancer statusescomprises a plurality of types of cancer. In some embodiments, theplurality of cancer statuses comprises a plurality of stages of cancer.

In some embodiments, the first test data set further comprises one ormore phenotypic characteristics of the tumor biopsy, and the classifierwas further trained against, for each respective training tissue samplein a plurality of training tissue samples, the one or more phenotypiccharacteristics of the respective training tissue sample. In someembodiments, the one or more phenotypic characteristics of the tumorbiopsy comprise a histologic feature of the tumor biopsy.

In some embodiments, the first test data set further comprises one ormore characteristics of the subject, as described in the featureselection section above, and the classifier was further trained against,for each respective training tissue sample in a plurality of trainingtissue samples, the one or more characteristics of the subject fromwhich the respective training tissue sample was obtained.

In some embodiments, the method also includes obtaining a second testdata set comprising the plurality of nucleic acid features of a tumorbiopsy from the test subject, and evaluating the second test data setusing the classifier trained to discriminate between the two or moretumor sensitivities to the therapeutic agent, where the plurality ofnucleic acid features in the first test data set are from a first tumorbiopsy from the test subject and the second test data set are from asecond tumor biopsy from the test subject that is different from thefirst tumor biopsy. Ins some embodiments, the first tumor biopsy and thesecond tumor biopsy are from different sections of a same tumor of thesubject. In some embodiments, the first tumor biopsy and the secondtumor biopsy are from different tumors of the subject.

In some embodiments, the classifier is a multinomial classifier that istrained to provide a plurality of likelihoods, wherein each respectivelikelihood in the plurality of likelihoods is a likelihood that adifferent respective treatment regime, in a plurality of treatmentregimes, for the cancer in the subject will be more effective than theother treatment regimes in the plurality of treatment regimes.

In some embodiments, the classifier is a neural network algorithm, asupport vector machine algorithm, a Naive Bayes algorithm, a nearestneighbor algorithm, a boosted trees algorithm, a random forestalgorithm, a convolutional neural network algorithm, a decision treealgorithm, a regression algorithm, or a clustering algorithm.

In some embodiments, the methods described herein also includeadministering the recommended therapy for treating cancer to the testsubject.

I. Providing Therapeutic Agent Info on a Clinical Report

In some embodiments, the disclosure provides methods and systems forperforming methods of providing a clinical report for a cancer patientto a physician. In some embodiments, the methods include obtaining afirst test data set comprising features of a transcriptome from a tumorbiopsy from the cancer patient. In some embodiments, the methods includeevaluating the first test data set using a classifier trained, e.g., asdescribed herein, to discriminate between two or more tumorsensitivities to a first therapeutic agent. The methods then includereceiving a recommended therapy for the cancer patient from theclassifier, and including the recommended therapy, or sending therecommended therapy to a third party for inclusion, in a clinical reportfor the cancer patient.

In some embodiments, the recommended therapy for the cancer patientcomprises administration of the therapeutic agent.

In some embodiments, the patient has a basal cell skin cancer, asquamous cancer, a breast cancer, a bladder cancer, a cervical cancer, acolon cancer, an endometrial cancer, a head and neck cancer, ahepatobiliary cancer, a kidney cancer, a gastric cancer, a lung cancer,a mesothelial cancer of the pleural cavity, a mesothelial cancer of theperitoneal cavity, an ovarian cancer, prostate cancer, or a rectalcancer. In some embodiments, the patient has a breast cancer. In someembodiments, the patient has an ovarian cancer. In some embodiments, thepatient does not have has breast cancer or ovarian cancer. In someembodiments, the patient has a colorectal cancer. In some embodiments,the patient has a lung cancer. In some embodiments, the patient has anon-small cell lung cancer. In some embodiments, the patient has anendometrial cancer. In some embodiments, the patient has not previouslybeen treated for cancer. In some embodiments, the patient has previouslybeen treated for the cancer.

J. Research Tool for Repurposing Drugs

In some embodiments, the disclosure provides methods and systems forperforming methods of identifying a new use for a pharmaceuticalcompound of a first pharmaceutical class. In some embodiments, themethods include obtaining a plurality of tissue samples, wherein eachrespective tissue sample in the plurality of tissue samples is sensitiveto a respective class of pharmaceutical agents in a plurality of classesof pharmaceutical agents that excludes the first pharmaceutical class.The methods then includes culturing, for each respective tissue samplein the plurality of tissue samples, one or more respective organoidsfrom one or more cells of the respective tissue sample, therebygenerating a plurality of organoid cultures. The methods then includeexposing, for each organoid culture in the plurality of organoidcultures, the respective one or more organoids to one or moreconcentrations of the pharmaceutical agent, and measuring, for eachorganoid culture in the plurality of organoid cultures, the fitness ofcells in the respective one or more organoids following the exposure tothe pharmaceutical agent. Reduced fitness of the cells in the respectiveone or more organoids is indicative that the pharmaceutical moleculeshares pharmacological properties with the class of pharmaceuticalcompounds in the plurality of classes of pharmaceutical classes to whichthe tissue sample corresponding to the respective one or more organoidsis sensitive.

In some embodiments, the plurality of tissue samples comprises cellsfrom at least 3 different types of cancers. In some embodiments, theplurality of tissue samples comprises cells from at least 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, or more different types of cancers.

K. Selecting Patients for Clinical Trials with an Organoid Assay

In some embodiments, the disclosure provides methods and systems forperforming methods of determining the eligibility of a cancer patientfor a clinical trial of a candidate cancer pharmaceutical agent. In someembodiments, the methods include obtaining a tumor biopsy from thecancer patient, and culturing one or more tumor organoids from one ormore cells of the tumor biopsy. The methods then include exposing theone or more tumor organoids to one or more concentrations of thecandidate cancer pharmaceutical agent, and measuring the fitness ofcells in the one or more tumor organoids following the exposure to theone or more concentrations of the candidate cancer pharmaceutical agent.The methods then include determining whether the cancer patient iseligible for the clinical trial based on at least the measured fitnessof the cells in the one or more tumor organoids, wherein reduced fitnessof the cells in the one or more tumor organoids is indicative that thecancer patient is eligible for the clinical trial.

In some embodiments, the clinical trial is for the treatment of a breastcancer. In some embodiments, the clinical trial is for the treatment ofan ovarian cancer. In some embodiments, the clinical trial is for thetreatment of a breast cancer or ovarian cancer. In some embodiments, theclinical trial is for the treatment of a colorectal cancer. In someembodiments, the clinical trial is for the treatment of a lung cancer.In some embodiments, the clinical trial is for the treatment of anon-small cell lung cancer. In some embodiments, the clinical trial isfor the treatment of an endometrial cancer. In some embodiments, theclinical trial is for the treatment of a patient that has not previouslybeen treated for cancer. In some embodiments, the clinical trial is forthe treatment of a patient that has previously been treated for thecancer.

L. Selecting Patients for Clinical Trials with a Classifier

In some embodiments, the disclosure provides methods and systems forperforming methods of determining the eligibility of a cancer patientfor a clinical trial of a candidate cancer pharmaceutical agent. In someembodiments, the methods include obtaining a first test data setcomprising a plurality of nucleic acid features of a tumor biopsy fromthe cancer patient. The methods then include evaluating the first testdata set using a classifier trained to discriminate between two or moretumor sensitivities to the candidate cancer pharmaceutical agent. Theclassifier is trained against, for each respective training tissuesample in a plurality of training tissue samples, at least (i) theplurality of nucleic acid features obtained from the respective trainingtissue sample, and (ii) a corresponding indication of the sensitivity ofa respective organoid cultured from one or more cells of the respectivetraining tissue sample to the candidate cancer pharmaceutical agent. Afirst tumor sensitivity in the two or more tumor sensitivities to thecandidate cancer pharmaceutical agent is associated with an indicationthat the cancer patient is eligible for the clinical trial. A secondtumor sensitivity in the two or more tumor sensitivities to thecandidate cancer pharmaceutical agent is associated with an indicationthat the cancer patient is eligible for the clinical trial. The methodsthen include determining whether the cancer patient is eligible for theclinical trial based on at least the evaluation.

In some embodiments, the clinical trial is for the treatment of a basalcell skin cancer, a squamous cancer, a breast cancer, a bladder cancer,a cervical cancer, a colon cancer, an endometrial cancer, a head andneck cancer, a hepatobiliary cancer, a kidney cancer, a gastric cancer,a lung cancer, a mesothelial cancer of the pleural cavity, a mesothelialcancer of the peritoneal cavity, an ovarian cancer, prostate cancer, ora rectal cancer. In some embodiments, the clinical trial is for thetreatment of a patient that has not previously been treated for cancer.

In some embodiments, the clinical trial is for the treatment of apatient that has previously been treated for the cancer.

In some embodiments, the candidate cancer pharmaceutical agent haspreviously been found to be effective for the treatment of a cancerhaving a first clinical marker and the clinical trial is for thetreatment of a cancer that does not have the first clinical marker.

In some embodiments, the plurality of nucleic acid features of the tumorbiopsy include one or more of support for a single nucleotide variant ata genomic location, a methylation status at a genomic location, arelative copy number for a genomic location, an allelic ratio for agenomic location, a relative expression level of a gene, and amathematical combination thereof.

In some embodiments, the first test data set also includes a cancerstatus of the test subject in a plurality of cancer statuses, and theclassifier was further trained against, for each respective trainingtissue sample in a plurality of the training tissue samples, a cancerstatus of the respective training tissue sample in the plurality ofcancer statuses. In some embodiments, the plurality of cancer statusescomprises a plurality of types of cancer. In some embodiments, theplurality of cancer statuses comprises a plurality of stages of cancer.

In some embodiments, the first test data set further comprises one ormore phenotypic characteristics of the tumor biopsy, and the classifierwas further trained against, for each respective training tissue samplein a plurality of training tissue samples, the one or more phenotypiccharacteristics of the respective training tissue sample. In someembodiments, the one or more phenotypic characteristics of the tumorbiopsy comprise a histologic feature of the tumor biopsy.

In some embodiments, the first test data set also includes one or morecharacteristics of the subject, as described in the feature selectionsection above, and the classifier was further trained against, for eachrespective training tissue sample in a plurality of training tissuesamples, the one or more characteristics of the subject from which therespective training tissue sample was obtained.

M. Detecting Drug Resistance with a Classifier

In some embodiments, the disclosure provides methods and systems forperforming methods of identifying a resistance to a chemotherapeuticagent in a patient with cancer. In some embodiments, the methods includeobtaining a first test data set comprising a plurality of nucleic acidfeatures of a tumor biopsy from the patient with cancer. The methodsthen include evaluating the first test data set using a classifiertrained to discriminate between two or more tumor sensitivities to thechemotherapeutic agent. The classifier is trained against, for eachrespective training tissue sample in a plurality of training tissuesamples, at least (i) the plurality of nucleic acid features obtainedfrom the respective training tissue sample, and (ii) a correspondingindication of the sensitivity of a respective organoid cultured from oneor more cells of the respective training tissue sample to thechemotherapeutic agent. A first tumor sensitivity in the two or moretumor sensitivities to the candidate cancer pharmaceutical agent isassociated with an indication that the patient's cancer is resistant tothe chemotherapeutic agent. A second tumor sensitivity in the two ormore tumor sensitivities to the candidate cancer pharmaceutical agent isassociated with an indication that the patient's cancer is not resistantto the chemotherapeutic agent. The methods then include providing areport indicating whether the patient's cancer is resistant to thechemotherapeutic agent based on at least the results of the evaluation.

In some embodiments, the patient has a has a basal cell skin cancer, asquamous cancer, a breast cancer, a bladder cancer, a cervical cancer, acolon cancer, an endometrial cancer, a head and neck cancer, ahepatobiliary cancer, a kidney cancer, a gastric cancer, a lung cancer,a mesothelial cancer of the pleural cavity, a mesothelial cancer of theperitoneal cavity, an ovarian cancer, prostate cancer, or a rectalcancer. In some embodiments, the patient has not previously been treatedfor cancer. In some embodiments, the patient has previously been treatedfor the cancer.

In some embodiments, the chemotherapeutic agent has been previouslyassociated with a clinical marker and the cancer has the clinicalmarker.

In some embodiments, the plurality of nucleic acid features includes oneor more of support for a single nucleotide variant at a genomiclocation, a methylation status at a genomic location, a relative copynumber for a genomic location, an allelic ratio for a genomic location,a relative expression level of a gene, and a mathematical combinationthereof.

In some embodiments, the first test data set also includes one or morephenotypic characteristics of the tumor biopsy, and the classifier wasfurther trained against, for each respective training tissue sample in aplurality of training tissue samples, the one or more phenotypiccharacteristics of the respective training tissue sample. In someembodiments, the one or more phenotypic characteristics of the tumorbiopsy comprise a histologic feature of the tumor biopsy.

In some embodiments, the first test data set also includes one or morecharacteristics of the subject, as described in the feature selectionsection above, and the classifier was further trained against, for eachrespective training tissue sample in a plurality of training tissuesamples, the one or more characteristics of the subject from which therespective training tissue sample was obtained.

N. Evaluating Effect of Cancer Drug Using Tumor Organoid Assay andAssigning Cancer Treatment Based on Tumor Organoid Assay

In some aspects, the present disclosure provides systems and methods forlarge scale analysis of therapeutic responses using tumor organoids(TOs).

In certain aspects, a drug screening platform may be utilized to assessthe effectiveness of drugs or other agents on tumor organoids. In oneembodiment, tumor organoids are first dissociated into single cells,which are then seeded into individual partitions in the presence oftumor organoid culture media. Tumor organoids used in the methodsprovided herein can be derived from cells from any suitable cancerincluding, but not limited to, an anal cancer, a basal cell skin cancer,a squamous cancer, a breast cancer, a bladder cancer, a cervical cancer,a colon cancer, an endometrial cancer, a head and neck cancer, ahepatobiliary cancer, a kidney cancer, a gastric cancer, a lung cancer,a mesothelial cancer of the pleural cavity, a mesothelial cancer of theperitoneal cavity, an ovarian cancer, prostate cancer, a rectal cancer.

Tumor organoids that subsequently form from the individual cells in eachpartition exhibit tumor organoid heterogeneity. In some embodiments, thesingle cells are seeded in a multi-well plate (e.g., 24-, 48-, 96-,384-well). Preferably, individual cells are seeded at a density to allowsufficient number of TOs to form while not overcrowding the plate sothat TOs do not overlap or touch allowing for easy identification ofindividual TOs.

Any suitable tumor organoid medium can be used to culture the tumororganoids. In some embodiments, the tumor organoid medium includes oneor more tumor organoid growth factors. Organoid growth factors includean epidermal growth factor (EGF), a fibroblast growth factor (FGF), ahepatocyte growth factor (HGF), a Wnt, Noggin, an R-spondin, Gastrin,Prostaglandin, and Neuregulin. Organoid growth factors include ligands(natural, semi-synthetic, or synthetic, agonist or antagonist) of EGFfamily receptors, HGF family of receptors, Wnt family receptors, NOTCHfamily receptors, LRP receptor, Frizzled receptor, LGRS receptor,insulin receptor, neuregulin family of receptors, or any growth factorreceptor tyrosine kinase family member. Exemplary organoid culture mediaare summarized below in Table 1. In particular embodiments, the organoidculture medium is free of R-spondins. In some embodiments, the organoidculture medium is free of Wnt. An example of systems and methods forculturing tumor organoids may be found in U.S. patent application Ser.No. 16/693,117, titled “Tumor Organoid Culture Compositions, Systems,and Methods” and filed Nov. 22, 2019, which is incorporated by referenceherein in its entirety and in relevant parts related to systems andmethods for culturing tumor organoids.

TABLE 1 Organoid Culture Molecular Medium Growth Factors InhibitorsAdditional Components B Noggin Rho kinase a chemically-defined, minimalinhibitor, culture medium; L-glutamine; a transforming serum replacementsupplement; growth factor-beta N-acetyl-L-cysteine; and inhibitor, andnicotinamide MAP kinase inhibitor C EGF and Noggin Rho kinase achemically-defined, minimal inhibitor, culture medium; L-glutamine; atransforming serum replacement supplement; growth factor-betaN-acetyl-L-cysteine; and inhibitor, and nicotinamide MAP kinaseinhibitor D EGF, Noggin, Rho kinase a chemically-defined, minimal FGF7and inhibitor, culture medium; L-glutamine; a FGF10 transforming serumreplacement supplement; growth factor-beta N-acetyl-L-cysteine; andinhibitor, and nicotinamide MAP kinase inhibitor

In one example, the TOs are also cultured in the presence of one or moreextracellular matrix (ECM) components that function as a substrate forculturing the TO. In particular embodiments, the substrate is aMatrigel.

In one example, one or more therapeutic agents are applied to the TOs inthe partitions. Exemplary therapeutic agents include, but are notlimited to, a molecular inhibitors, antibodies, recombinant nucleicacids (e.g., antisense oligonucleotides) and engineered immune cells(e.g., CAR T-cells and NK cells). Exemplary therapeutic agents include,but are not limited to, Paclitaxel, Gemcitabine, Cisplatin, Carboplatin,Oxaliplatin, Capecitabine, SN-38 (CPT-11), 5-FU, MTX (methotrexate),Docetaxel, Bortezomib, Everolimus, Ulixertinib, Dasatinib, Vinblastine,Nelarabine, Epirubicin, Afatinib, Lapatinib, Cytarabine, Cladribine,Doxorubicin, Azacitidine, and Staurosporine. Other examples includeclasses of drugs including but not limited to: taxanes, platinatingagents, vinca alkaloids, alkylating agents, and anthracyclines. One ormore molecular inhibitors may be applied to the TOs in the well plates.Molecular inhibitors may be selected by name, target, pathway, formula,or other known characterizations.

In some embodiments, the one or more therapeutic agents include one ormore of the following: an inhibitor of SUV4-20 (SUV420H1 or SUV420H2), atyrosine kinase inhibitor, a retinoid-like compound, a wee1 kinaseinhibitor, an anaplastic lymphoma kinase inhibitor, an aurora A kinaseinhibitor, an aurora B kinase inhibitor, a reversible inhibitor ofeukaryotic nuclear DNA replication, an antimetabolite antineoplasticagent, an ataxia telangiectasia and Rad3-related protein (ATR) kinaseinhibitor, an ATM kinase inhibitor, a checkpoint kinase inhibitor, aGSK-3a/b inhibitor, a proteasome inhibitor, an AXL or RET inhibitor, ac-Met or VEGFR2 inhibitor, an alkylating antineoplastic agent, a DNA-PKand/or mTOR inhibitor, an inhibitor of mammalian target of rapamycin(mTOR), a checkpoint kinase 1 (CHK1) inhibitor, a retinoic acid receptorβ (RARβ) or RARγ antagonist, a retinoic acid receptor (RAR)γ-selectiveagonist, RARγ-selective retinoid, inducer of apoptosis, CDK2 a RARagonist, a chemotherapy, a tyrosine kinase inhibitor antineoplasticagent, an antimicrotubular antineoplastic agent, a topoisomeraseinhibitor antineoplastic agent, a sodium-glucose cotransporter-2/SGLT2inhibitor, an inhibitor of the tropomyosin receptor kinases A, B and C,C-ros oncogene 1 and anaplastic lymphoma kinase, a topoisomeraseinhibitor antineoplastic agent, an inhibitor of mTOR, an inhibitor ofphosphatidylinositol 3-kinase (PI3K), an inhibitor of RIP3K, an analogof cyclophosphamide, an SGLT2 inhibitor, aWnt/β-catenin inhibitor, atyrosine kinase inhibitor that interrupts the HER2/neu and epidermalgrowth factor receptor/EGFR pathways, an inhibitor of tropomyosin kinasereceptors TrkA, TrkB, and TrkC, a cyclin-dependent kinase (CDK)inhibitor, a CDK7 inhibitor, an inhibitor of VEGFR1, VEGFR2 and VEGFR3kinases, a DNA-PK/PI3K/mTOR inhibitor, a poly ADP ribose polymerase(PARP) inhibitor, an inhibitor of Rac GTPase, a taxane, a BromodomainAnd PHD Finger Containing 1 (BRPF1) bromodomain inhibitor, amitogen-activated protein kinase-activated protein kinase 2 (MAPK2)inhibitor, a RAF inhibitor, a histone deacetylase (HDAC) inhibitor, aCDK1 inhibitor, aTGF-beta/Smad inhibitor, a Pim kinase inhibitor, a DNAtopoisomerase I inhibitor, active metabolite of CPT-11/Irinotecan, anatypical retinoid, apoptosis inducer, a multi-kinase inhibitor, afms-like tyrosine kinase-3 (FLT3) inhibitor, a MEK inhibitor, aninhibitor of extracellular signal-regulated kinase (ERK) 1 and/or 2, ora DNA-dependent protein kinase/DNA-PK inhibitor.

In some embodiments, the one or more therapeutic agents include one ormore for the following: A-196 (inhibitor of SUV4-20 or SUV420H1 andSUV420H2), Afatinib (tyrosine kinase inhibitor), Adapalene(retinoid-like compound), Adavosertib (MK-1775, wee1 kinase inhibitor),Alectinib (CH5424802, anaplastic lymphoma kinase inhibitor), Alisertib(MLN8237, aurora A kinase inhibitor), Aphidicolin (reversible inhibitorof eukaryotic nuclear DNA replication, antimitotic), Azacitidine (anantimetabolite antineoplastic agent, a chemotherapy), AZ20 (ataxiatelangiectasia and Rad3-related protein/ATR kinase inhibitor), AZ31(ataxia-telangiectasia mutated/ATM kinase inhibitor), AZD6738 (ataxiatelangiectasia and Rad3-related protein/ATR kinase inhibitor), AZD7762(checkpoint kinase inhibitor), Barasertib (AZD1152-HQPA, aurora B kinaseinhibitor), BAY-1895344 (ATR and ATM kinase inhibitor), Berzosertib (ATRand ATM kinase inhibitor), BIO-acetoxime (GSK-3a/b inhibitor),Bortezomib (proteasome inhibitor), Cabozantinib (kinase inhibitor,inhibitor of AXL, RET, and tyrosine kinases c-Met and VEGFR2),Capecitabine (an antimetabolite antineoplastic agent, a chemotherapy),Carboplatin (an alkylating antineoplastic agent, a chemotherapy), CC-115(DNA-PK and mTOR inhibitor), CC-223 (inhibitor of mammalian target ofrapamycin/mTOR), CCT-245737 (checkpoint kinase 1/CHK1 inhibitor),CD-2665 (retinoic acid receptor β (RARβ)/RARγ antagonist), CD-437(retinoic acid receptor (RAR)γ-selective agonist, γ-selective retinoid;inducer of apoptosis), CDK2 inhibitor II, CH-55 (RAR agonist), Cisplatin(an alkylating antineoplastic agent, a chemotherapy), Cladribine (anantimetabolite antineoplastic agent, a chemotherapy), Cytarabine (anantimetabolite antineoplastic agent, a chemotherapy), Dasatinib (atyrosine kinase inhibitor antineoplastic agent, a chemotherapy),Docetaxel (an antimicrotubular antineoplastic agent, a chemotherapy),Doxorubicin (Adriamycin, a topoisomerase inhibitor antineoplastic agent,a chemotherapy), Empagliflozin (BI 10773, a sodium-glucosecotransporter-2/SGLT2 inhibitor), Entrectinib (RXDX-101, tyrosine kinaseinhibitor, inhibitor of the tropomyosin receptor kinases A, B and C,C-ros oncogene 1 and anaplastic lymphoma kinase), Epirubicin (atopoisomerase inhibitor antineoplastic agent, a chemotherapy), Etoposide(a topoisomerase inhibitor antineoplastic agent, a chemotherapy),Everolimus (inhibitor of mTOR), Fluorouracil/5-FU (an antimetaboliteantineoplastic agent, a chemotherapy), GDC-0349 (inhibitor of mTOR),GDC-0575 (ARRY-575, CHK1 inhibitor), Gemcitabine (an antimetaboliteantineoplastic agent, a chemotherapy), GSK2292767 (inhibitor ofphosphatidylinositol 3-kinase/PI3K), GSK-872 (GSK2399872A, kinaseinhibitor, inhibitor of RIP3K), Hesperadin (aurora kinase inhibitor),Hydroxyurea (an antimetabolite antineoplastic agent, a chemotherapy),Ifosfamide (an analog of cyclophosphamide, an alkylating antineoplasticagent, a chemotherapy), Ipragliflozin (ASP1941, an SGLT2 inhibitor),KYA1797K (Wnt/β-catenin inhibitor), Lapatinib (tyrosine kinase inhibitorthat interrupts the HER2/neu and epidermal growth factor receptor/EGFRpathways, an antineoplastic agent, a chemotherapy), Larotrectinib(inhibitor of tropomyosin kinase receptors TrkA, TrkB, and TrkC), LDC4297 (Cyclin-dependent kinase/CDK inhibitor, CDK7 inhibitor), Lenvatinib(multiple kinase inhibitor, inhibitor of VEGFR1, VEGFR2 and VEGFR3kinases), LY3023414 (DNA-PK/PI3K/mTOR Inhibitor), Methotrexate (anantimetabolite antineoplastic agent, a chemotherapy), Nelarabine (anantimetabolite antineoplastic agent, a chemotherapy), Niraparib(MK-4827, a poly ADP ribose polymerase/PARP inhibitor), NSC 23766(inhibitor of Rac GTPase), Olaparib (PARP inhibitor), Oxaliplatin (analkylating antineoplastic agent, a chemotherapy), Paclitaxel (a taxane,an antimicrotubular antineoplastic agent, a chemotherapy), Pamiparib(BGB-290, PARP inhibitor), PFI-4 (Bromodomain And PHD Finger Containing1/BRPF1 bromodomain inhibitor), PHA-767491 HCl (Mitogen-activatedprotein kinase-activated protein kinase 2/MK2 and CDK inhibitor),PLX7904 (RAF inhibitor), Pracinostat (histone deacetylase/HDACinhibitor), Pralatrexate (an antimetabolite antineoplastic agent, achemotherapy), Prexasertib HCl (checkpoint kinase 1/CHK1 inhibitor),RO-3306 (CDK1 inhibitor), Rucaparib (PARP inhibitor), Selpercatinib(LOXO-292, ARRY-192, a tyrosine kinase inhibitor), SIS3 HCl(TGF-beta/Smad inhibitor), SMI-4a (Pim kinase inhibitor), SN-38(inhibitor of DNA topoisomerase I, active metabolite ofCPT-11/Irinotecan), ST-1926 (Adarotene, atypical retinoid, apoptosisinducer), Staurosporine (multi-kinase inhibitor used as a positivecontrol), Talazoparib (BMN-673, PARP inhibitor), TCS 359 (fms-liketyrosine kinase-3/FLT3 inhibitor), Tenalisib (RP6530, a PI3K δ/γinhibitor), Tozasertib (VX-680, MK-0457, an Aurora Kinase inhibitor),Trametinib (GSK1120212, a MEK inhibitor), Ulixertinib (inhibitor ofextracellular signal-regulated kinase/ERK 1 and 2, with potentialantineoplastic activity), Veliparib (ABT-888, PARP inhibitor),Vinblastine (an antimicrotubular antineoplastic agent, a chemotherapy),or VX-984 (DNA-dependent protein kinase/DNA-PK inhibitor).

In some embodiments, the one or more therapeutic agents include one orthe follow therapeutic agents or combination therapeutic: afatinib plusMET inhibitor (for example, tivantinib, cabozantinib, crizotinib, etc.),AZ31 plus SN-38, bevacizumab (anti-VEGF monoclonal IgG1 antibody),cetuximab (epidermal growth factor receptor/EGFR inhibitor), crizotinib(a tyrosine kinase inhibitor antineoplastic agent), cyclophosphamide (analkylating antineoplastic agent), erlotinib (epidermal growth factorreceptor inhibitor antineoplastic agent), FOLFIRI, bevacizumab plusFOLFIRI, FOLFOX, gefitinib (EGFR inhibitor), gemcitabine plus docetaxel,pemtrexed (an antimetabolite antineoplastic agent), ramucirumab(Vascular Endothelial Growth Factor Receptor 2/VEGFR2 Inhibitor), ortopotecan (a topoisomerase inhibitor).

Additional exemplary therapeutic agents that can be used with themethods described herein are provided in Table 2.

TABLE 2 Agent Target Pathway Formula Veliparib (ABT-888) PARP DNA DamageC₁₃H₁₆N₄O Selumetinib (AZD6244) MEK MAPK C₁₇H₁₅BrClFN₄O3 PD184352(CI-1040) MEK MAPK C₁₇H₁₄ClF₂IN₂O₂ PD0325901 MEK MAPK C₁₆H₁₄F₃IN₂O₄Tozasertib (VX-680, Aurora Kinase Cell Cycle C₂₃H₂₈N₈OS MK-0457) Y-276322HCl Autophagy, ROCK Cell Cycle C₁₄H₂₃C_(l2)N₃O Olaparib (AZD2281, PARPDNA Damage C₂₄H₂₃FN₄O₃ Ku-0059436) SL-327 MEK MAPK C₁₆H₁₂F₃N₃S SB431542TGF-beta/Smad TGF-beta/Smad C₂₂H₁₆N₄O₃ MK-2206 2HCl Akt PI3K/Akt/mTORC₂₅H₂₃C_(l2)N₅O Refametinib (RDEA119, Bay MEK MAPK C₁₉H₂₀F₃IN₂O₅S86-9766) KU-55933 (ATM Kinase ATM/ATR DNA Damage C₂₁H₁₇NO₃S₂ Inhibitor)GSK1904529A IGF-1R Protein Tyrosine C₄₄H₄₇F₂N₉O₅S Kinase PF-04217903c-Met Protein Tyrosine C₁₉H₁₆N₈O Kinase U0126-EtOH MEK MAPK C₂₀H₂₂N₆OS₂BI 2536 PLK Cell Cycle C28H39N7O3 JNJ-38877605 c-Met Protein TyrosineC19H13F2N7 Kinase Odanacatib (MK-0822) Cysteine Protease ProteasesC₂₅H₂₇F₄N₃O₃S Alisertib (MLN8237) Aurora Kinase Cell Cycle C27H20ClFN4O4Barasertib (AZD1152- Aurora Kinase Cell Cycle C26H30FN7O3 HQPA)CP-724714 EGFR, HER2 Protein Tyrosine C27H27N5O3 Kinase TGX-221 PI3KPI3K/Akt/mTOR C21H24N4O2 WZ4002 EGFR Protein Tyrosine C25H27ClN6O3Kinase BIBR 1532 Telomerase DNA Damage C21H17NO3 Anastrozole AromataseEndocrinology & C17H19N5 Hormones Aprepitant Substance P OthersC23H21F7N4O3 TAK-700 (Orteronel) P450 (e.g. CYP17) Metabolism C18H17N3O2PFI-1 (PF-6405761) Epigenetic Reader Epigenetics C16H17N3O4S DomainKU-0063794 mTOR PI3K/Akt/mTOR C25H31N5O4 CHIR-99021 (CT99021) GSK-3PI3K/Akt/mTOR C22H18Cl2N8 WYE-354 mTOR PI3K/Akt/mTOR C24H29N7O5TG100-115 PI3K PI3K/Akt/mTOR C18H14N6O2 Aurora A Inhibitor I AuroraKinase Cell Cycle C31H31ClFN7O2 Ispinesib (SB-715992) KinesinCytoskeletal C30H33ClN4O2 Signaling Zibotentan (ZD4054) EndothelinReceptor GPCR & G C19H16N6O4S Protein Safinamide Mesylate MAO MetabolismC18H23FN2O5S GSK429286A ROCK Cell Cycle C21H16F4N4O2 Pimasertib(AS-703026) MEK MAPK C15H15FIN3O3 Tadalafil PDE Metabolism C22H19N3O4Adavosertib (MK-1775) Wee1 Cell Cycle C27H32N8O2 CP-673451 PDGFR ProteinTyrosine C24H27N5O2 Kinase Selisistat (EX 527) Sirtuin EpigeneticsC13H13ClN2O Dapagliflozin SGLT GPCR & G C21H25ClO6 Protein Nebivolol HClAdrenergic Receptor Neuronal C22H26ClF2NO4 Signaling Pimobendan PDEMetabolism C19H18N4O2 AZD8055 mTOR PI3K/Akt/mTOR C25H31N5O4 KU-60019ATM/ATR DNA Damage C30H33N3O5S Tie2 kinase inhibitor Tie-2ProteinTyrosine C26H21N3O2S Kinase Apixaban Factor Xa MetabolismC25H25N5O4 Raltegravir (MK-0518) Integrase Microbiology C20H21FN6O5PCI-34051 HDAC Epigenetics C17H16N2O3 Ambrisentan Endothelin ReceptorGPCR & G C22H22N2O4 Protein SB743921 HCl Kinesin CytoskeletalC31H34Cl2N2O3 Signaling AST-1306 EGFR Protein Tyrosine C₃₁H₂₆ClFN₄O₅SKinase Sapitinib (AZD8931) EGFR, HER2 Protein Tyrosine C23H25ClFN5O3Kinase GSK461364 PLK Cell Cycle C27H28F3N5O2S Mubritinib (TAK 165) HER2Protein Tyrosine C25H23F3N4O2 Kinase UK 383367 Procollagen C MetabolismC15H24N4O4 Proteinase Cryptotanshinone STAT JAK/STAT C19H20O3 IcariinPDE Metabolism C33H40O15 OSI-027 mTOR PI3K/Akt/mTOR C21H22N6O3Rabusertib (LY2603618) Chk Cell Cycle C18H22BrN5O3 URB597 FAAHMetabolism C20H22N2O3 A66 PI3K PI3K/Akt/mTOR C17H23N5O2S2 ICG-001Wnt/beta-catenin Stem Cells & C33H32N4O4 Wnt PF-3845 FAAH MetabolismC24H23F3N4O2 Trametinib (GSK1120212) MEK MAPK C26H23FIN5O4 Ibrutinib(PCI-32765) BTK Angiogenesis C25H24N6O2 CHIR-124 Chk Cell CycleC23H22ClN5O Mardepodect (PF-2545920) PDE Metabolism C25H20N4O WAY-600mTOR PI3K/Akt/mTOR C28H30N8O Nepicastat (SYN-117) HCl HydroxylaseMetabolism C14H16ClF2N3S RS-127445 5-HT Receptor Neuronal C17H16FN3Signaling CP-91149 Phosphorylase Metabolism C21H22ClN3O3 SB415286 GSK-3PI3K/Akt/mTOR C16H10ClN3O5 GSK1070916 Aurora Kinase Cell Cycle C30H33N7ONiraparib (MK-4827) PARP DNA Damage C19H20N4O CHIR-98014 GSK-3PI3K/Akt/mTOR C20H17Cl2N9O2 AMG-458 c-Met Protein Tyrosine C30H29N5O5Kinase Tivantinib (ARQ 197) c-Met Protein Tyrosine C23H19N3O2 KinaseCanagliflozin SGLT GPCR & G C24H25FO5S Protein NVP-BVU972 c-Met ProteinTyrosine C20H16N6 Kinase MK-5108 (VX-689) Aurora Kinase Cell CycleC₂₂H₂₁ClFN₃O₃S SB705498 TRPV Others C17H16BrF3N4O Vistusertib (AZD2014)mTOR PI3K/Akt/mTOR C25H30N6O3 A-803467 Sodium Channel TransmembraneC19H16ClNO4 Transporters Sirtinol Sirtuin Epigenetics C26H22N2O2Ipatasertib (GDC-0068) Akt PI3K/Akt/mTOR C24H32ClN5O2 Sapanisertib (INK128, mTOR PI3K/Akt/mTOR C15H15N7O MLN0128) Tyrphostin AG 879 HER2Protein Tyrosine C18H24N2OS Kinase JNJ-1661010 FAAH MetabolismC19H19N5OS CTEP (RO4956371) GluR Neuronal C19H13ClF3N3O SignalingAlogliptin (SYK-322) DPP-4 Proteases C18H21N5O2 benzoate T0070907 PPARDNA Damage C12H8ClN3O3 GW441756 Trk receptor Protein Tyrosine C17H13N3OKinase SB742457 5-HT Receptor Neuronal C19H19N3O2S Signaling ZM 323881HCl VEGFR Protein Tyrosine C22H19ClFN3O2 Kinase GNF-2 Bcr-AblAngiogenesis C18H13F3N4O2 Lumiracoxib COX Neuronal C15H13ClFNO2Signaling JNJ-7777120 Histamine Receptor Neuronal C14H16ClN3O SignalingIOX2 HIF Angiogenesis C19H16N2O5 PF-4981517 P450 (e.g. CYP17) MetabolismC26H32N8 CHIR-99021 (CT99021) HCl GSK-3 PI3K/Akt/mTOR C22H19Cl3N8Rivaroxaban Factor Xa Metabolism C19H18ClN3O5S Linagliptin DPP-4Proteases C25H28N8O2 Azilsartan Medoxomil RAAS Endocrinology &C30H24N4O8 Hormones Sulfaphenazole P450 (e.g. CYP17) MetabolismC15H14N4O2S Sitagliptin phosphate DPP-4 Proteases C16H20F6N5O6Pmonohydrate Avanafil PDE Metabolism C23H26ClN7O3 Eprosartan MesylateRAAS Endocrinology & C24H28N2O7S2 Hormones Carprofen COX NeuronalC15H12ClNO2 Signaling Saxagliptin hydrate DPP-4 Proteases C18H27N3O3Daminozide Histone Demethylase Epigenetics C6H12N2O3 Bedaquilinefumarate Anti-infection Microbiology C36H35BrN2O6 JZL184 LipaseMetabolism C27H24N2O9 SC-514 IκB/IKK NF-κB C9H8N2OS2 (R)-Nepicastat HClHydroxylase Metabolism C14H16ClF2N3S Asunaprevir HCV Protease ProteasesC35H46ClN5O9S Trelagliptin succinate DPP-4 Proteases C22H26FN5O6Dabrafenib Mesylate Raf MAPK C₂₄H₂₄F₃N₅O₅S₃ Argatroban MonohydrateThrombin Others C23H38N6O6S Sitagliptin DPP-4 Proteases C16H15F6N5ORaltegravir potassium Integrase, HIV Microbiology C20H20FKN6O5 ProteaseAlogliptin DPP-4 Proteases C18H21N5O2 Dasabuvir(ABT-333) HCV ProteaseProteases C26H27N3O5S Ertugliflozin SGLT2 Ion-Channel C22H25ClO7Rolapitant NK1-receptor GPCR C25H26F6N2O2 Dapagliflozin propanediol SGLTGPCR & G C24H35ClO9 monohydrate Protein Bedaquiline tuberculosisImmunology C32H31BrN2O2 Fruquintinib VEGFRs VEGFR C21H19N3O5 JNJ0966Others Others C16H16N4O2S2 acalisib (GS-9820) PI3K PI3K/Akt/mTORC21H16FN7O BRL-50481 PDE Metabolism C9H12N2O4S Canagliflozin hemihydrateSGLT GPCR & G C48H52F2O11S2 Protein JANEX-1 JAK JAK/STAT C16H15N3O3Anagliptin DPP-4 Proteases C19H25N7O2 GSK 5959 Epigenetic Reader DoEpigenetics C22H26N4O3 Pitolisant hydrochloride Histamine ReceptorNeuronal C17H27Cl2NO Signaling K 858 Kinesin Cytoskeletal C13H15N3O2SSignaling BAY-61-3606 Syk Angiogenesis C20H20Cl2N603 Stattic STATJAK/STAT C8H5NO4S GSK2656157 PERK Apoptosis C23H21FN6O XL388 mTORPI3K/Akt/mTOR C23H22FN3O4S LY2090314 GSK-3 PI3K/Akt/mTOR C28H25FN6O3MK-8745 Aurora Kinase Cell Cycle C20H19ClFN5OS Tepotinib (EMD 1214063)c-Met Protein Tyrosine C29H28N6O2 Kinase SGC 0946 Histone EpigeneticsC28H40BrN7O4 Methyltransferase GSK2334470 PDK PI3K/Akt/mTOR C25H34N8OIPA-3 PAR Cytoskeletal C20H14O2S2 Signaling VE-822 ATM/ATR PI3K/Akt/mT0RC24H25N5O3S (+)-JQ1 Epigenetic Reader Epigenetics C23H25ClN4O2S DomainPYR-41 E1 Activating Ubiquitin C17H13N3O7 TCID DUB Ubiquitin C9H2Cl4O2DMH1 TGF-beta/Smad TGF-beta/Smad C24H20N4O ML347 TGF-beta/Smad, ALKTGF-beta/Smad C22H16N4O UNC1999 Histone Epigenetics C33H43N7O2Methyltransferase SSR128129E FGFR Angiogenesis C18H15N2NaO4 Spebrutinib(CC-292, BTK Angiogenesis C22H22FN5O3 AVL-292) SKI II S1P Receptor GPCR& G C15H11ClN2OS Protein PF-543 S1P Receptor GPCR & G C27H31NO4S ProteinCID755673 Serine/threonin Apoptosis C12H11NO3 kinase, CaMK1-Azakenpaullone GSK-3 PI3K/Akt/mTOR C15H10BrN3O CNX-2006 EGFR ProteinTyrosine C26H27F4N7O2 Kinase Bisindolylmaleimide I PKC TGF-beta/SmadC25H24N4O2 (GF109203X) Thiamet G Others Others C9H16N2O4S Alvelestat(AZD9668) Serine Protease Proteases C24H20F3N5O4S RGFP966 HDACEpigenetics C21H19FN4O UNC0642 Histone Epigenetics C29H44F2N6O2Methyltransferase NVP-TNKS656 PARP DNA Damage C27H34N4O5 AGI-6780Dehydrogenase Metabolism C₂₁H₁₈F₃N₃O₃S₂ Ro3280 PLK Cell CycleC27H35F2N7O3 NMS-P937 (NMS1286937) PLK Cell Cycle C24H27F3N8O3 CNX-774BTK Angiogenesis C26H22FN7O3 AZD1981 GPR Endocrinology & C19H17ClN2O3SHormones SRPIN340 Others Others C18H18F3N3O 4μ8C Others Others C11H8O4NMS-E973 HSP (e g. HSP90) Cytoskeletal C22H22N4O7 Signaling PFI-2 HClHistone Epigenetics C23H25F4N3O3S Methyltransferase GSK2606414 PERKApoptosis C24H20F3N5O IPI-3063 PI3K PI3K/Akt/mTOR C25H25N7O2 AtglistatinLipase Metabolism C17H21N3O CGP 57380 MNK MAPK C11H9FN6 SB-3CT MMPProteases C15H14O3S2 AR-A014418 GSK-3 PI3K/Akt/mTOR C12H12N4O4S NH125CaMK Neuronal C27H45IN2 Signaling XEN445 Lipase Metabolism C18H17F3N2O3RLDC000067 CDK Cell Cycle C18H18N4O3S PI-1840 Proteasome ProteasesC22H26N4O3 FTI 277 HCl Transferase Metabolism C₂₂H₃₀C_(l)N₃O₃S₂Nexturastat A HDAC DNA Damage C19H23N3O3 ESI-09 cAMP GPCR & GC16H15ClN4O2 Protein HJC0350 cAMP GPCR & G C15H19NO2S Protein HO-3867STAT JAK/STAT C28H30F2N2O2 JNK Inhibitor IX JNK MAPK C20H16N2OSTrelagliptin DPP-4 Proteases C18H20FN5O2 XMD8-92 ERK MAPK C26H30N6O3A-366 Histone Epigenetics C19H27N3O2 Methyltransferase GSK-LSD1 2HClHistone Demethylase Epigenetics C14H22Cl2N2 LLY-507 Histone EpigeneticsC36H42N6O Methyltransferase Santacruzamate A HDAC DNA Damage C15H22N2O3(CAY10683) CAY10603 HDAC DNA Damage C22H30N4O6 GSK1324726A (I-BET726)Epigenetic Reader Epigenetics C25H23ClN2O3 Domain SD-208 TGF-beta/SmadTGF-beta/Smad C17H10ClFN6 TH588 MTH1 DNA Damage C13H12Cl2N4 SB225002CXCR GPCR & G C13H10BrN3O4 Protein CPI-360 Histone EpigeneticsC25H31N3O4 Methyltransferase Picropodophyllin (PPP) IGF-1R ProteinTyrosine C22H22O8 Kinase Savolitinib(AZD6094, c-Met Protein TyrosineC17H15N9 HMPL-504) Kinase SP2509 Histone Demethylase EpigeneticsC19H20ClN3O5S VX-11e ERK MAPK C₂₄H₂₀C₁₂FN₅O₂ SBE 13 HCl PLK Cell CycleC24H28Cl2N2O4 BLZ945 CSF-1R Protein Tyrosine C20H22N4O3S Kinase LFM-A13BTK Angiogenesis C11H8Br2N2O2 EPZ015666(GSK3235025) Histone EpigeneticsC20H25N5O3 Methyltransferase VER155008 HSP (e.g. HSP90) CytoskeletalC25H23Cl2N7O4 Signaling BPTES Glutaminase Proteases C24H24N6O2S3 AZ6102PPAR DNA Damage C25H28N6O Erlotinib EGFR Protein Tyrosine C22H23N3O4Kinase ORY-1001 (RG-6016) 2HCl Histone Demethylase EpigeneticsC15H24Cl2N2 EPZ020411 2HCl Histone Epigenetics C25H40Cl2N4O3Methyltransferase I-BRD9 Epigenetic Reader Epigenetics C₂₂H₂₂F₃N₃O₃S₂Domain SirReal2 Sirtuin Epigenetics C22H20N4OS2 BDA-366 Bcl-2 ApoptosisC24H29N3O4 NVP-CGM097 Mdm2 Apoptosis C38H47ClN4O4 CC-223 mTORPI3K/Akt/mTOR C21H27N5O3 PFI-4 Epigenetic Reader Epigenetics C21H24N4O3Domain BIO-acetoxime GSK-3 PI3K/Akt/mTOR C18H12BrN3O3 GSK2292767 PI3KPI3K/Akt/mTOR C24H28N6O5S SIS3 HCl TGF-beta/Smad TGF-beta/SmadC28H28ClN3O3 Larotrectinib (LOXO-101) Trk receptor Protein TyrosineC21H24F2N6O6S sulfate Kinase PLX7904 Raf MAPK C24H22F2N6O3S VPS34-IN1PI3K PI3K/Akt/mTOR C21H24ClN7O A-196 Histone Epigenetics C18H16Cl2N4Methyltransferase LDC4297 (LDC044297) CDK Cell Cycle C23H28N8O SMI-4aPim JAK/STAT C11H6F3NO2S Empagliflozin (BI 10773) SGLT GPCR & GC23H27ClO7 Protein TCS 359 FLT3 Angiogenesis C18H20N2O4S NSC 23766 RhoCell Cycle C24H38Cl3N7 GDC-0349 mTOR PI3K/Akt/mTOR C24H32N6O3Cobimetinib (GDC-0973, MEK MAPK C21H21F3IN3O2 RG7420) GW2580 CSF-1RProtein Tyrosine C20H22N4O3 Kinase BMS-345541 IκB/IKK NF-κB C14H17N5Dynasore Dynamin Cytoskeletal C18H14N2O4 Signaling Venetoclax (ABT-199,Bcl-2 Apoptosis C45H50ClN7O7S GDC-0199) ICI-118551 HydrochlorideAdrenergic Receptor GPCR & G C17H28ClNO2 Protein AMG 337 c-Met ProteinTyrosine C23H22FN7O3 Kinase PF-CBP1 HCl Epigenetic Reader EpigeneticsC29H37ClN4O3 Domain CPI-637 Epigenetic Reader Epigenetics C22H22N6ODomain BI-78D3 JNK MAPK C13H9N5O5S2 SB366791 TRPV TransmembraneC16H14ClNO2 Transporters Thiomyristoyl Sirtuin DNA Damage C34H51N3O3SCCT245737 Chk Cell Cycle C16H16F3N7O GSK6853 Epigenetic ReaderEpigenetics C22H27N5O3 Domain SHP099 dihydrochloride phosphatase OthersC16H21Cl4N5 Selonsertib (GS-4997) ASK Apoptosis C24H24FN7O KYA1797KWnt/bleta-catenin Stem Cells & C17H11KN2O6S2 Wnt IPI-549 PI3KPI3K/Akt/mTOR C30H24N8O2 SGC2085 Histone Epigenetics C19H24N2O2Methyltransferase Irbinitinib (ARRY-380, HER2 Protein TyrosineC26H24N8O2 ONT-380) Kinase NMS-P118 PARP DNA Damage C20H24F3N3O2 BAY-876GLUT Metabolism C24H16F4N6O2 VPS34 inhibitor 1 PI3K PI3K/Akt/mTORC21H25N7O (Compound 19, PIK-III analogue) UK-371804 HCl Serine ProteaseProteases C₁₄H₁₇Cl₂N₅O₄S GSK′872 (GSK2399872A) Serine/threonin kinaseApoptosis C19H17N3O2S2 LLY-283 Histone Epigenetics C17H18N4O4Methyltransferase GSK180736A (GSK180736) ROCK Cell Cycle C19H16FN5O2PD-166866 (PD166866) FGFR Angiogenesis C20H24N6O3 BLU-554 (BLU554) FGFRAngiogenesis C24H24Cl2N4O4 LY3214996 ERK MAPK C22H27N7O2S PF-06651600JAK JAK/STAT C15H19N5O FM-381 JAK JAK/STAT C24H24N6O2 AZ31 ATM/ATR DNADamage C24H28N4O3 Tofogliflozin(CSG 452) SGLT GPCR & G C22H28O7 ProteinOmarigliptin (MK-3102) DPP-4 Proteases C17H20F2N4O3S Serabelisib (INK-PI3K PI3K/Akt/mTOR C19H17N5O3 1117, MLN-1117, TAK-117) FX1 Bcl-6Apoptosis C14H9ClN2O4S2 Pamiparib (BGB-290) PARP DNA Damage C16H15FN4ONCT-503 Dehydrogenase Metabolism C20H23F3N4S Chk2 Inhibitor II (BML-277)Chk Cell Cycle C20H14ClN3O2 Ipragliflozin (ASP1941) SGLT GPCR & GC21H21FO5S Protein Nec-1s (7-Cl—O—Nec1) TNF-alpha Apoptosis C13H12ClN3O2GSK′963 NF-κB, TNF-alpha NF-κB C14H18N2O GNF-6231 Wnt/beta-catenin StemCells & C24H25FN6O2 Wnt Skp2 inhibitor C1 (SKPin CDK Cell CycleC₁₈H₁₃BrN₂O₄S₂ C1) PF-06840003 IDO Metabolism C12H9FN2O2 GI254023XImmunology & Immunology & C21H33N3O4 Inflammation related InflammationBAY 1895344 (BAY- ATM/ATR DNA Damage C20H22ClN7O 1895344) Tenalisib(RP6530) PI3K PI3K/Akt/mTOR C23H18FN5O2 H3B-6527 FGFR Protein TyrosineC29H34Cl2N8O4 Kinase Cu-CPT22 TLR Immunology & C19H22O7 InflammationAZD1390 ATM/ATR PI3K/Akt/mTOR C27H32FN5O2 Atuveciclib (BAY-1143572) CDKCell Cycle C18H18FN5O2S LXH254 Raf MAPK C25H25F3N4O4 WM-1119 HistoneAcetyltransf Epigenetics C18H13F2N3O3S Evobrutinib BTK Protein TyrosineC25H27N5O2 Kinase LIT-927 CXCR Immunology & C17H13ClN2O3 Inflammation4-Hydroxyquinazoline Others antiplatelet C8H6N2O Valbenazine tosylateVMAT2 Others C38H54N2O10S2 SAR125844 c-Met Protein TyrosineC25H23FN8O2S2 Kinase dBET1 Epigenetic Reader Do EpigeneticsC38H37ClN8O7S GSK′547 TNF-alpha Apoptosis C20H18F2N6O Palbociclib(PD-0332991) CDK Cell Cycle C24H30ClN7O2 HCl Palbociclib (PD0332991) CDKCell Cycle C26H35N7O6S Isethionate LDN-193189 2HCl TGF-beta/SmadTGF-beta/Smad C25H23ClN6 MCC950(CP-456773) Immunology & Immunology &C20H23N2NaO5S Inflammation related Inflammation bpV (HOpic) PTEN OthersC6H4K2NO8V Erlotinib HCl (OSI-744) Autophagy, EGFR Protein TyrosineC22H24ClN3O4 Kinase SGX-523 c-Met Protein Tyrosine C18H13N7S Kinase(−)-Huperzine A (HupA) GluR, AChR Neuronal C15H18N2O Signaling GSK256066PDE Metabolism C27H26N4O5S CCT137690 Aurora Kinase Cell CycleC26H31BrN8O Capmatinib (INCB28060) c-Met Protein Tyrosine C23H17FN6OKinase EPZ005687 Histone Epigenetics C32H37N5O3 Methyltransferase GSK126Histone Epigenetics C31H38N6O2 Methyltransferase Tazemetostat (EPZ-6438)Histone Epigenetics C34H44N4O4 Methyltransferase ISRIB (trans-isomer)PERK Apoptosis C22H24Cl2N2O4 A-1210477 Bcl-2 Apoptosis C46H55N7O7SOtenabant (CP-945598) HCl Cannabinoid Receptor GPCR & G C25H26Cl3N7OProtein FGF401 FGFR Protein Tyrosine C₂₅H₃₀N₈O₄ Kinase Lazertinib(YH25448, EGFR Protein Tyrosine C₃₀H₃₄N₈O₃ GNS-1480) Kinase

In some embodiments, different concentrations of the therapeutic agentare applied to the TOs in each partition or a group of replicatepartitions (e.g., 2, 3, 4, 5, 6, 7, 8, 9 or 10 or more partitions). Incertain embodiments, different therapeutic agents or combination oftherapeutic agents are applied to the TOs in each partition or a groupof replicate partitions. In certain embodiments, tumor organoids derivedfrom different subjects are assessed.

In some embodiments, the tumor organoids of the method are divided inplurality of tumor organoid subsets, wherein the plurality of subsetsincludes at least 10, 20, 30, 40, 50 60, 70, 80, 90, 100, 200, 300, 400,500, 600, 700, 800, 900, or 1×10³, 1×10⁴, 1×10⁵, or 1×10⁶ subsets oftumor organoids. In some embodiments, each of the subsets include atleast 10, 20, 30, 40, 50 60, 70, 80, 90, 100, 200, 300, 400, 500, 600,700, 800, 900, or 1×10³, 1×10⁴ tumor organoids.

In some embodiments, tumor organoid profiles are acquired from at leastabout 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or1×10³, 1×10⁴, 1×10⁵, or 1×10⁶ individual tumor organoids per condition(e.g., a particular therapeutic dosage). In exemplary embodiments,wherein the tumor organoids of the method are divided in plurality oftumor organoid subsets that each receive a different therapeutic agentor dosage of a therapeutic agent, a tumor organoid profile is acquiredfor at least about 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60,70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1×10³,1×10⁴, 1×10⁵, or 1×10⁶ individual tumor organoids in each of thesubsets. In some embodiments, tumor organoid profiles are obtained forabout 5-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90,90-100, 100-200, 200-300, 300-400, 400-500, 500-600, 600-700, 700-800 or800-900, 10-50, 50-100, 100-500, 500-1,000, 1,000-1,500, or 1,500-2,000tumor organoids for each subset. In exemplary embodiments, the tumororganoid profile includes a cell viability value for at least 10, 15,20, 25, 30, 35, 40, 45, 50, 60, 70, 75, 80, 85, 90, 95, 99 or 100% ofthe tumor organoids for each subset.

The high number of measurements recorded per condition (e.g., dose)allows the use of more complex statistical methods that would otherwisebe unable to be used with a low-throughput dose response assay. In someembodiments, the tumor organoid profiles obtained are adjusted for oneor more confounding technical effect. Use of a linear model allows forinclusion of covariates to adjust for potential confounding technicaleffects including initial TO viability, differences in growth ratesbetween TOs derived from different patients, and different cancer types,and leverages all of the TO data to gain better statistical power. Insome embodiments, a linear model is applied to determine differencesbetween patients, or between drugs, at equivalent therapeuticconcentrations (or doses).

In particular embodiments, a tumor organoid profile includes a cellviability value, wherein the cell viability value is the percentage ofviable cells in a particular tumor organoid. In certain embodiments, thecell viability value is determined by visual detection techniquesincluding, for example, methods that use fluorescent light microscopyand/or compound light microscopy (i.e., brightfield microscopy)techniques. As disclosed herein, fluorescent light microscopy techniquesinclude those that use fluorescent dyes to visualize dead/apoptoticcells and/or total cells. In some embodiments, wherein compound lightmicroscopy techniques are used, artificial fluorescent images arederived from brightfield images using a trained model and cell viabilityvalues are determined based on the brightfield images, as disclosedherein.

In certain embodiments, the tumor organoid profiles are acquired atdifferent time points. In some embodiments, the tumor organoid profilesacquired are used to generate a dose-response curve (see, e.g., Example2 and FIG. 2, below). In particular embodiments, a particulartherapeutic dosage is assigned to a patient based on the dose-responsecurve. Also provided herein are methods for treating a patient having acancer with a therapeutic agent based on a dosage derived using thesubject method provided herein.

Concurrently with or following the application of the therapeuticagent(s) to the TOs in the partitions, the TOs are contacted with one ormore detection agents to assess for changes in morphological features orcell viability of individual cells in the TOs. In exemplary embodiments,the detection agents include fluorescent markers that can be visualizedby fluorescent confocal imaging analysis. In particular embodiments, themarkers include two or more markers for dead/apoptotic cells. Markersand assays useful for assessing dead apoptotic cells include, but arenot limited to, IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent(Essen Biosciences cat #4440), and TO-PRO™-3 Iodide (642/661) (FisherScientific cat #T3605) and Annexin V assay (Abcam, ab14085).

In some embodiments, an additional detection agent is used to identifytotal cells in the tumor organoid sample. In some examples, all cellsper organoid are measured by Hoechst 33342 staining, apoptotic cells perorganoid are measured by Caspase 3/7 staining and dead/dying cells mayare by TO-PRO-3 staining. Utilizing fluorescent markers for all cellsand two markers for dead/apoptotic cells permits analysis of TOs at thesingle cell level and also permits generation of an absolute number oflive and dead cells per organoid. This TO by TO analysis may providemore information than simply calculating a relative value of viablecells from an entire well. Maintaining TO heterogeneity allows fordetermination of whether all cells are dying at a constant rate or ifthere is a mix of susceptible and resistant cells to a given treatmentbased on the distribution of viable cells per organoid. The aspects,such as the number of partitions (e.g., wells), types of plates, andtypes of cultures disclosed here, are exemplary in nature. Other aspectsknown in the art may be used instead or in combination with thoseaspects disclosed herein.

Following contact with the detection agents, the TOs are imaged and atumor organoid profile is obtained from each image. In particularembodiments, the tumor organoid profile includes the number of TOs perimage, the total number of cells (live and dead) present in each TO, thenumber of dead and dying cells present, and/or a cell viability valueindicating the percentage of viable cells/tumor organoid. In someembodiments, the TOs are imaged on an inverted confocal microscope usingthe light microscopy and multiple fluorescent channels with varyingwave-length excitation sources (e.g. laser or LED) and emission filters.In exemplary embodiments, an analysis module in a computer system isused to derive the tumor organoid profile from the images.

In some embodiments, a brightfield image of the TO sample is obtainedfor each sample in addition to the cell death/viability data. Inparticular embodiments, tumor organoid profiles that include cellviability data (e.g., the percentage of viable cells in TO) andcorresponding brightfield images for each TO sample at particularconditions (e.g., drug dosage conditions) are inputted into a classifiedtrainer. In exemplary embodiments, an artificial fluorescent image isdeveloped from the brightfield image. In particular embodiments, thetrainer is used to predict morphological changes (e.g., cell death orapoptosis) in a tumor organoid sample in response to a particularcondition (e.g., dose of a therapeutic agent) based on a brightfieldimage of the sample (see Example 2 and FIG. 3).

In another aspect, provided herein are computer systems for carrying outthe subject methods for assessing the effects of an agent (e.g., atherapeutic agent) on tumor organoids (TOs). In some embodiments, thecomputer system includes at least one processor and a memory storing atleast one program for execution by the at least one processor, the atleast one program includes instructions for carrying out one or moresteps of the subject methods.

In some embodiments, the disclosure provides methods and systems forevaluating an effect of a cancer therapeutic agent. In some embodiments,the method includes providing a plurality of tumor organoids cultured ina tumor organoid culture medium, where the plurality of tumor organoidsis divided into a plurality of tumor organoid subsets. The method thenincludes contacting each subset in the plurality of tumor organoidsubsets with a cancer therapeutic agent. The method also includescontacting each subset in the plurality of tumor organoid subsets withone or more cell death detection agents and a total cell detectionagent. The method then includes obtaining a tumor organoid profile(e.g., cell viability value) for each subset in the plurality of subset.In exemplary embodiments, the tumor organoid profile is obtained for atleast 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 75, 80, 85, 90, 95,100, 150, 200, 500 or, 1,000 individual tumor organoids for each subset.In some embodiments, the tumor organoid profile includes a cellviability value for every tumor organoid in the subset. In exemplaryembodiments, the tumor organoid profile includes a cell viability valuefor at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 75, 80, 85, 90,95, 100, 150, 200, 500 or, 1,000 individual tumor organoids for eachsubset. In exemplary embodiments, the tumor organoid profile includes acell viability value for at least 10, 15, 20, 25, 30, 35, 40, 45, 50,60, 70, 75, 80, 85, 90, 95, 99 or 100% of the tumor organoids for eachsubset. The method then includes assessing the effect of the cancertherapeutic agent based on the tumor organoid profiles.

In some embodiments, the disclosure provides methods and systems forassigning a treatment dosage of a cancer therapeutic agent for a subjectin need thereof. In some embodiments, the method includes providing aplurality of tumor organoids cultured in a tumor organoid culturemedium, where the tumor organoids are derived from a subject, and wherethe plurality of tumor organoids is divided into a plurality of tumororganoid subsets. The method then includes contacting each subset in theplurality of tumor organoid subsets with a different dosage of a cancertherapeutic agent. The method also includes contacting each subset inthe plurality of tumor organoid subsets with one or more cell deathdetection agents and a total cell detection agent. The method thenincludes obtaining a tumor organoid profile for each subset in theplurality of subset, where the tumor organoid profile includes a cellviability value for every tumor organoid in the subset. The method thenincludes determining a therapeutic agent dosage curve from the tumororganoid profiles. The method then includes assigning a treatment dosageof the cancer therapeutic agent to the subject based on the therapeuticagent dosage curve.

In some embodiments, a tumor's resistance to a particular therapeuticagent at a particular dosage is determined based on the proportion oftumor organoids derived from the tumor that have a 1% or greaterviability value when exposed to the particular therapeutic agent at thedosage. In some embodiments, the tumor is determined to be likelyresistant to the therapeutic agent composition at a particularconcentration if 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 99% ormore of the tumor organoids for a subset contacted with the therapeuticagent at the particular concentration exhibit 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, or 99% or more cell viability. In some embodiments,the tumor is determined to be likely resistant to the therapeutic agentcomposition at a particular concentration if 50% or more of the tumororganoids for a subset contacted with the therapeutic agent at theparticular concentration exhibit 50% or more cell viability. In someembodiments, the tumor determined to be likely to be resistant to thetherapeutic agent composition at a particular concentration if 1% moreof the tumor organoids for a subset contacted with the therapeutic agentat the particular concentration exhibit 100% cell viability.

In some embodiments, wherein a patient's tumor is designated as likelyto be resistant to a particular therapeutic agent or agents, amonitoring frequency that is more frequent than the standard monitoringfrequency is recommended to the patient. wherein a patient's tumor isnot designated as likely to be resistant to a particular therapeuticagent or agents, a standard monitoring frequency is recommended to thesubject.

In exemplary embodiments, a plurality of tumor organoids from a tumororganoid subset that is designated as likely to be resistant to atherapeutic agent is isolated and analyzed for one or more geneticvariants associated with resistance to the therapeutic agent, e.g., agenetic variant that is present in at least 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, or 99% or more of the resistant tumor organoid. Inexemplary embodiments, a plurality of tumor organoids from a tumororganoid subset that is designated as likely to be susceptible to atherapeutic agent (e.g., 0% cell viability) is isolated and analyzed forone or more genetic variants associated with susceptibility to thetherapeutic agent e.g., a genetic variant that is present in at least50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 99% or more of thesusceptible tumor organoids.

O. Therapeutic Agents

Any suitable therapeutic agent can be used using the subject methodsdescribed herein. In some embodiments the therapeutic agent is a singletherapeutic agent. In other embodiments, the therapeutic agent includes2, 3, 4, 5, 6, 7, 8, 9, or 10 therapeutic agents.

Suitable therapeutic agents include, but are not limited to, molecularinhibitors, antibodies, recombinant nucleic acids (e.g., antisenseoligonucleotides) and engineered immune cells (e.g., CAR T-cells and NKcells). Exemplary therapeutic agents include, but are not limited to,Paclitaxel, Gemcitabine, Cisplatin, Carboplatin, Oxaliplatin,Capecitabine, SN-38 (CPT-11), 5-FU, MTX (methotrexate), Docetaxel,Bortezomib, Everolimus, Ulixertinib, Dasatinib, Vinblastine, Nelarabine,Epirubicin, Afatinib, Lapatinib, Cytarabine, Cladribine, Doxorubicin,Azacitidine, and Staurosporine. Other examples include classes of drugsincluding but not limited to: taxanes, platinating agents, vincaalkaloids, alkylating agents, and anthracyclines. One or more molecularinhibitors may be applied to the TOs in the well plates. Molecularinhibitors may be selected by name, target, pathway, formula, or otherknown characterizations.

In some embodiments, the one or more therapeutic agents include one ormore of the following: an inhibitor of SUV4-20 (SUV420H1 or SUV420H2), atyrosine kinase inhibitor, a retinoid-like compound, a wee1 kinaseinhibitor, an anaplastic lymphoma kinase inhibitor, an aurora A kinaseinhibitor, an aurora B kinase inhibitor, a reversible inhibitor ofeukaryotic nuclear DNA replication, an antimetabolite antineoplasticagent, an ataxia telangiectasia and Rad3-related protein (ATR) kinaseinhibitor, an ATM kinase inhibitor, a checkpoint kinase inhibitor, aGSK-3a/b inhibitor, a proteasome inhibitor, an AXL or RET inhibitor, ac-Met or VEGFR2 inhibitor, an alkylating antineoplastic agent, a DNA-PKand/or mTOR inhibitor, an inhibitor of mammalian target of rapamycin(mTOR), a checkpoint kinase 1 (CHK1) inhibitor, a retinoic acid receptorβ (RARβ) or RARγ antagonist, a retinoic acid receptor (RAR)γ-selectiveagonist, RARγ-selective retinoid, inducer of apoptosis, CDK2 a RARagonist, a chemotherapy, a tyrosine kinase inhibitor antineoplasticagent, an antimicrotubular antineoplastic agent, a topoisomeraseinhibitor antineoplastic agent, a sodium-glucose cotransporter-2/SGLT2inhibitor, an inhibitor of the tropomyosin receptor kinases A, B and C,C-ros oncogene 1 and anaplastic lymphoma kinase, a topoisomeraseinhibitor antineoplastic agent, an inhibitor of mTOR, an inhibitor ofphosphatidylinositol 3-kinase (PI3K), an inhibitor of RIP3K, an analogof cyclophosphamide, an SGLT2 inhibitor, aWnt/β-catenin inhibitor, atyrosine kinase inhibitor that interrupts the HER2/neu and epidermalgrowth factor receptor/EGFR pathways, an inhibitor of tropomyosin kinasereceptors TrkA, TrkB, and TrkC, a cyclin-dependent kinase (CDK)inhibitor, a CDK7 inhibitor, an inhibitor of VEGFR1, VEGFR2 and VEGFR3kinases, a DNA-PK/PI3K/mTOR inhibitor, a poly ADP ribose polymerase(PARP) inhibitor, an inhibitor of Rac GTPase, a taxane, a BromodomainAnd PHD Finger Containing 1 (BRPF1) bromodomain inhibitor, amitogen-activated protein kinase-activated protein kinase 2 (MAPK2)inhibitor, a RAF inhibitor, a histone deacetylase (HDAC) inhibitor, aCDK1 inhibitor, aTGF-beta/Smad inhibitor, a Pim kinase inhibitor, a DNAtopoisomerase I inhibitor, active metabolite of CPT-11/Irinotecan, anatypical retinoid, apoptosis inducer, a multi-kinase inhibitor, afms-like tyrosine kinase-3 (FLT3) inhibitor, a MEK inhibitor, aninhibitor of extracellular signal-regulated kinase (ERK) 1 and/or 2, ora DNA-dependent protein kinase/DNA-PK inhibitor.

In some embodiments, the one or more therapeutic agents include one ormore for the following: A-196 (inhibitor of SUV4-20 or SUV420H1 andSUV420H2), Afatinib (tyrosine kinase inhibitor), Adapalene(retinoid-like compound), Adavosertib (MK-1775, wee1 kinase inhibitor),Alectinib (CH5424802, anaplastic lymphoma kinase inhibitor), Alisertib(MLN8237, aurora A kinase inhibitor), Aphidicolin (reversible inhibitorof eukaryotic nuclear DNA replication, antimitotic), Azacitidine (anantimetabolite antineoplastic agent, a chemotherapy), AZ20 (ataxiatelangiectasia and Rad3-related protein/ATR kinase inhibitor), AZ31(ataxia-telangiectasia mutated/ATM kinase inhibitor), AZD6738 (ataxiatelangiectasia and Rad3-related protein/ATR kinase inhibitor), AZD7762(checkpoint kinase inhibitor), Barasertib (AZD1152-HQPA, aurora B kinaseinhibitor), BAY-1895344 (ATR and ATM kinase inhibitor), Berzosertib (ATRand ATM kinase inhibitor), BIO-acetoxime (GSK-3a/b inhibitor),Bortezomib (proteasome inhibitor), Cabozantinib (kinase inhibitor,inhibitor of AXL, RET, and tyrosine kinases c-Met and VEGFR2),Capecitabine (an antimetabolite antineoplastic agent, a chemotherapy),Carboplatin (an alkylating antineoplastic agent, a chemotherapy), CC-115(DNA-PK and mTOR inhibitor), CC-223 (inhibitor of mammalian target ofrapamycin/mTOR), CCT-245737 (checkpoint kinase 1/CHK1 inhibitor),CD-2665 (retinoic acid receptor β (RARβ)/RARγ antagonist), CD-437(retinoic acid receptor (RAR)γ-selective agonist, γ-selective retinoid;inducer of apoptosis), CDK2 inhibitor II, CH-55 (RAR agonist), Cisplatin(an alkylating antineoplastic agent, a chemotherapy), Cladribine (anantimetabolite antineoplastic agent, a chemotherapy), Cytarabine (anantimetabolite antineoplastic agent, a chemotherapy), Dasatinib (atyrosine kinase inhibitor antineoplastic agent, a chemotherapy),Docetaxel (an antimicrotubular antineoplastic agent, a chemotherapy),Doxorubicin (Adriamycin, a topoisomerase inhibitor antineoplastic agent,a chemotherapy), Empagliflozin (BI 10773, a sodium-glucosecotransporter-2/SGLT2 inhibitor), Entrectinib (RXDX-101, tyrosine kinaseinhibitor, inhibitor of the tropomyosin receptor kinases A, B and C,C-ros oncogene 1 and anaplastic lymphoma kinase), Epirubicin (atopoisomerase inhibitor antineoplastic agent, a chemotherapy), Etoposide(a topoisomerase inhibitor antineoplastic agent, a chemotherapy),Everolimus (inhibitor of mTOR), Fluorouracil/5-FU (an antimetaboliteantineoplastic agent, a chemotherapy), GDC-0349 (inhibitor of mTOR),GDC-0575 (ARRY-575, CHK1 inhibitor), Gemcitabine (an antimetaboliteantineoplastic agent, a chemotherapy), GSK2292767 (inhibitor ofphosphatidylinositol 3-kinase/PI3K), GSK-872 (GSK2399872A, kinaseinhibitor, inhibitor of RIP3K), Hesperadin (aurora kinase inhibitor),Hydroxyurea (an antimetabolite antineoplastic agent, a chemotherapy),Ifosfamide (an analog of cyclophosphamide, an alkylating antineoplasticagent, a chemotherapy), Ipragliflozin (ASP1941, an SGLT2 inhibitor),KYA1797K (Wnt/β-catenin inhibitor), Lapatinib (tyrosine kinase inhibitorthat interrupts the HER2/neu and epidermal growth factor receptor/EGFRpathways, an antineoplastic agent, a chemotherapy), Larotrectinib(inhibitor of tropomyosin kinase receptors TrkA, TrkB, and TrkC), LDC4297 (Cyclin-dependent kinase/CDK inhibitor, CDK7 inhibitor), Lenvatinib(multiple kinase inhibitor, inhibitor of VEGFR1, VEGFR2 and VEGFR3kinases), LY3023414 (DNA-PK/PI3K/mTOR Inhibitor), Methotrexate (anantimetabolite antineoplastic agent, a chemotherapy), Nelarabine (anantimetabolite antineoplastic agent, a chemotherapy), Niraparib(MK-4827, a poly ADP ribose polymerase/PARP inhibitor), NSC 23766(inhibitor of Rac GTPase), Olaparib (PARP inhibitor), Oxaliplatin (analkylating antineoplastic agent, a chemotherapy), Paclitaxel (a taxane,an antimicrotubular antineoplastic agent, a chemotherapy), Pamiparib(BGB-290, PARP inhibitor), PFI-4 (Bromodomain And PHD Finger Containing1/BRPF1 bromodomain inhibitor), PHA-767491 HCl (Mitogen-activatedprotein kinase-activated protein kinase 2/MK2 and CDK inhibitor),PLX7904 (RAF inhibitor), Pracinostat (histone deacetylase/HDACinhibitor), Pralatrexate (an antimetabolite antineoplastic agent, achemotherapy), Prexasertib HCl (checkpoint kinase 1/CHK1 inhibitor),RO-3306 (CDK1 inhibitor), Rucaparib (PARP inhibitor), Selpercatinib(LOXO-292, ARRY-192, a tyrosine kinase inhibitor), SIS3 HCl(TGF-beta/Smad inhibitor), SMI-4a (Pim kinase inhibitor), SN-38(inhibitor of DNA topoisomerase I, active metabolite ofCPT-11/Irinotecan), ST-1926 (Adarotene, atypical retinoid, apoptosisinducer), Staurosporine (multi-kinase inhibitor used as a positivecontrol), Talazoparib (BMN-673, PARP inhibitor), TCS 359 (fms-liketyrosine kinase-3/FLT3 inhibitor), Tenalisib (RP6530, a PI3K δ/γinhibitor), Tozasertib (VX-680, MK-0457, an Aurora Kinase inhibitor),Trametinib (GSK1120212, a MEK inhibitor), Ulixertinib (inhibitor ofextracellular signal-regulated kinase/ERK 1 and 2, with potentialantineoplastic activity), Veliparib (ABT-888, PARP inhibitor),Vinblastine (an antimicrotubular antineoplastic agent, a chemotherapy),or VX-984 (DNA-dependent protein kinase/DNA-PK inhibitor).

In some embodiments, the one or more therapeutic agents include one orthe follow therapeutic agents or combination therapeutic: afatinib plusMET inhibitor (for example, tivantinib, cabozantinib, crizotinib, etc.),AZ31 plus SN-38, bevacizumab (anti-VEGF monoclonal IgG1 antibody),cetuximab (epidermal growth factor receptor/EGFR inhibitor), crizotinib(a tyrosine kinase inhibitor antineoplastic agent), cyclophosphamide (analkylating antineoplastic agent), erlotinib (epidermal growth factorreceptor inhibitor antineoplastic agent), FOLFIRI, bevacizumab plusFOLFIRI, FOLFOX, gefitinib (EGFR inhibitor), gemcitabine plus docetaxel,pemtrexed (an antimetabolite antineoplastic agent), ramucirumab(Vascular Endothelial Growth Factor Receptor 2/VEGFR2 Inhibitor), ortopotecan (a topoisomerase inhibitor).

P. Artificial Fluorescent Imaging

In some embodiments, the methods disclosed herein use artificialfluorescent images derived from brightfield images using a trained modelto assess the effects of a particular therapeutic agent or agents on atumor organoid or tumor organoid population.

Typically, drug response is measured via cell viability assays usinglive/dead fluorescent stains, which have multiple drawbacks. Forexample, fluorescence microscopy can generate bottlenecks in thehigh-throughput screening process, stains can be costly for large scalescreening, relatively long image acquisition times reduce throughput,and stain cytotoxicity often limits temporal profiling. In contrast,brightfield and transmitted light microscopy do not require staining,allow for shorter acquisition times, and enable temporal profiling byavoiding cytotoxicity. However, visualizing and quantifying live/deadcells from brightfield images alone is not easily accessible and is asignificant obstacle towards more cost-efficient high-throughputscreening of tumor organoids. Certain systems and methods describedherein provide artificial fluorescent images that can be generated usingonly brightfield images.

Analysis of drug response data by target may identify importantpathways/mutations. For drugs that cause cell death in organoids, thetargets of those drugs may be important. Thus, it is desirable todiscover and/or develop additional drugs that modulate these targets.The cellular pathways and/or mutations that are important may bespecific to the cancer type of the organoid. For example, if CDKinhibitors specifically kill colorectal cancer (CRC) tumor organoidcells, CDK may be especially important in CRC.

FIG. 33 shows an example of a system 100 for automatically analyzingtumor organoid images. In some embodiments, the system 100 can include acomputing device 104, a secondary computing device 108, and/or a display116. In some embodiments, the system 100 can include an organoid imagedatabase 120, a training data database 124, and/or a trained modelsdatabase 128. In some embodiments, the trained models database 128 caninclude one or more trained machine learning models such as artificialneural networks. In some embodiments, the computing device 104 can be incommunication with the secondary computing device 108, the display 116,the organoid image database 120, the training data database 124, and/orthe trained models database 128 over a communication network 112. Asshown in FIG. 33, the computing device 104 can receive tumor organoidimages, such as brightfield images of tumor organoids, and generateartificial fluorescent stain images of the tumor organoids. In someembodiments, the computing device 104 can execute at least a portion ofan organoid image analysis application 132 to automatically generate theartificial fluorescent stain images.

The organoid image analysis application 132 can be included in thesecondary computing device 108 that can be included in the system 100and/or on the computing device 104. The computing device 104 can be incommunication with the secondary computing device 108. The computingdevice 104 and/or the secondary computing device 108 may also be incommunication with a display 116 that can be included in the system 100over the communication network 112.

The communication network 112 can facilitate communication between thecomputing device 104 and the secondary computing device 108. In someembodiments, communication network 112 can be any suitable communicationnetwork or combination of communication networks. For example,communication network 112 can include a Wi-Fi network (which can includeone or more wireless routers, one or more switches, etc.), apeer-to-peer network (e.g., a Bluetooth network), a cellular network(e.g., a 3G network, a 4G network, a 5G network, etc., complying withany suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX,etc.), a wired network, etc. In some embodiments, communication network112 can be a local area network, a wide area network, a public network(e.g., the Internet), a private or semi-private network (e.g., acorporate or university intranet), any other suitable type of network,or any suitable combination of networks. Communications links shown inFIG. 33 can each be any suitable communications link or combination ofcommunications links, such as wired links, fiber optic links, Wi-Filinks, Bluetooth links, cellular links, etc.

The organoid image database 120 can include a number of raw tumororganoid images, such as brightfield images. In some embodiments, thebrightfield images can be generated using a brightfield microscopyimaging modality. Exemplary brightfield images are described below. Insome embodiments, the organoid image database 120 can include artificialfluorescent stain images generated by the organoid image analysisapplication 132.

The training data database 124 can include a number of images fortraining a model to generate artificial fluorescent stain images. Insome embodiments, the training data image database 124 can include rawbrightfield images and corresponding three channel fluorescent stainimages. The trained models database 128 can include a number of trainedmodels that can receive raw brightfield images of tumor organoids andoutput artificial fluorescent stain images. In some embodiments, trainedmodels 136 can be stored in the computing device 104.

For an example of a system 100 for automatically analyzing tumororganoid images, see PCT/US20/63619, titled “Systems and Methods forHigh Throughput Drug Screening”, filed on Dec. 7, 2020, and incorporatedherein by reference in its entirety and for all purposes.

FIG. 34 shows an example 200 of hardware that can be used in someembodiments of the system 100. The computing device 104 can include aprocessor 204, a display 208, an input 212, a communication system 216,and a memory 220. The processor 204 can be any suitable hardwareprocessor or combination of processors, such as a central processingunit (“CPU”), a graphics processing unit (“GPU”), etc., which canexecute a program, which can include the processes described below.

In some embodiments, the display 208 can present a graphical userinterface. In some embodiments, the display 208 can be implemented usingany suitable display devices, such as a computer monitor, a touchscreen,a television, etc. In some embodiments, the inputs 212 of the computingdevice 104 can include indicators, sensors, actuatable buttons, akeyboard, a mouse, a graphical user interface, a touch-screen display,etc.

In some embodiments, the communication system 216 can include anysuitable hardware, firmware, and/or software for communicating with theother systems, over any suitable communication networks. For example,the communication system 216 can include one or more transceivers, oneor more communication chips and/or chip sets, etc. In a more particularexample, communication system 216 can include hardware, firmware, and/orsoftware that can be used to establish a coaxial connection, a fiberoptic connection, an Ethernet connection, a USB connection, a Wi-Ficonnection, a Bluetooth connection, a cellular connection, etc. In someembodiments, the communication system 216 allows the computing device104 to communicate with the secondary computing device 108.

In some embodiments, the memory 220 can include any suitable storagedevice or devices that can be used to store instructions, values, etc.,that can be used, for example, by the processor 204 to present contentusing display 208, to communicate with the secondary computing device108 via communications system(s) 216, etc. The memory 220 can includeany suitable volatile memory, non-volatile memory, storage, or anysuitable combination thereof. For example, the memory 220 can includeRAM, ROM, EEPROM, one or more flash drives, one or more hard disks, oneor more solid state drives, one or more optical drives, etc. In someembodiments, the memory 220 can have encoded thereon a computer programfor controlling operation of computing device 104 (or secondarycomputing device 108). In such embodiments, the processor 204 canexecute at least a portion of the computer program to present content(e.g., user interfaces, images, graphics, tables, reports, etc.),receive content from the secondary computing device 108, transmitinformation to the secondary computing device 108, etc.

The secondary computing device 108 can include a processor 224, adisplay 228, an input 232, a communication system 236, and a memory 240.The processor 224 can be any suitable hardware processor or combinationof processors, such as a central processing unit (“CPU”), a graphicsprocessing unit (“GPU”), etc., which can execute a program, which caninclude the processes described below.

In some embodiments, the display 228 can present a graphical userinterface. In some embodiments, the display 228 can be implemented usingany suitable display devices, such as a computer monitor, a touchscreen,a television, etc. In some embodiments, the inputs 232 of the secondarycomputing device 108 can include indicators, sensors, actuatablebuttons, a keyboard, a mouse, a graphical user interface, a touch-screendisplay, etc.

In some embodiments, the communication system 236 can include anysuitable hardware, firmware, and/or software for communicating with theother systems, over any suitable communication networks. For example,the communication system 236 can include one or more transceivers, oneor more communication chips and/or chip sets, etc. In a more particularexample, communication system 236 can include hardware, firmware, and/orsoftware that can be used to establish a coaxial connection, a fiberoptic connection, an Ethernet connection, a USB connection, a Wi-Ficonnection, a Bluetooth connection, a cellular connection, etc. In someembodiments, the communication system 236 allows the secondary computingdevice 108 to communicate with the computing device 104.

In some embodiments, the memory 240 can include any suitable storagedevice or devices that can be used to store instructions, values, etc.,that can be used, for example, by the processor 224 to present contentusing display 228, to communicate with the computing device 104 viacommunications system(s) 236, etc. The memory 240 can include anysuitable volatile memory, non-volatile memory, storage, or any suitablecombination thereof. For example, the memory 240 can include RAM, ROM,EEPROM, one or more flash drives, one or more hard disks, one or moresolid state drives, one or more optical drives, etc. In someembodiments, the memory 240 can have encoded thereon a computer programfor controlling operation of secondary computing device 108 (orcomputing device 104). In such embodiments, the processor 224 canexecute at least a portion of the computer program to present content(e.g., user interfaces, images, graphics, tables, reports, etc.),receive content from the computing device 104, transmit information tothe computing device 104, etc.

The display 116 can be a computer display, a television monitor, aprojector, or other suitable displays.

FIG. 35 shows an exemplary flow 300 that can generate brightfield imagesand/or fluorescent images, as well as live/dead assays readouts, usingpatient derived organoids grown from tumor specimens. In someembodiments, the live/dead assays readouts can be produced usingbrightfield and multiplexed fluorescence imaging. Drug response can bemeasured via cell viability assays using live/dead fluorescent stains.In some embodiments, the flow 300 can be included in a high throughputdrug screening system.

The flow 300 can include harvesting a tumor specimen 308 from a humanpatient 304, culturing organoids 312 using the tumor specimen 308, drugscreening 316 the organoids, imaging the organoids 320, and outputtingbrightfield and fluorescence images 324 of the organoids. After theorganoids are cultured, cells from the organoids can be plated into anassay plate (e.g. a 96-well assay plate, a 384-well assay plate, etc.).The assay plate may also be referred to as a plate. The drug screening316 can include plating the cells and treating the cells with a numberof different drugs and/or concentrations. For example, a 384-well platecan include fourteen drugs at seven different concentrations. As anotherexample, a 96-well plate can include six drugs at five differentconcentrations. The imaging 320 can include brightfield imaging thetreated cells, as well as applying fluorescent stains to at least aportion of the cells and fluorescent imaging the cells. In someembodiments, the fluorescent imaging can include producing threechannels of data for each cell. The three channels of data can include ablue/all nuclei channel, a green/apoptotic channel, and a red/pink/deadchannel. Each channel can be used to form a fluorescent image.Additionally, the imaging 320 can produce combined 3-channel fluorescentimages that include the blue/all nuclei channel, the green/apoptoticchannel, and the red/pink/dead channel. In some embodiments, the imaging320 can include generating brightfield images of the cells using abright-field microscope and generating fluorescent images of the cellsusing a confocal microscope such as a confocal laser scanningmicroscope. In some embodiments, instead of using traditionalfluorescent staining to generate the fluorescent images, the imaging 320can include generating brightfield images for at least a portion of thecells and generating artificial brightfield images for the portion ofthe cells based on the brightfield images using a process describedbelow (e.g., the process of FIG. 41).

In some embodiments, brightfield images (for example a 2D brightfieldprojection) depicting a cell culture well during a drug screening assaycan be generated using a 10× objective on a microscope. In someembodiments, the microscope can be an ImageXPRESS microscope availablefrom Molecular Devices. In some embodiments, the cells can be cancercell lines or cancer tumor organoids derived from patient specimens.

FIG. 36 shows an exemplary flow 400 for training a generator 408 togenerate an artificial fluorescent image 412 based on an inputbrightfield image 404 of organoid cells. In some embodiments, thegenerator 408 can include a U-Net convolutional neural network. In someembodiments, the generator 408 can include a pix2pix model. In someembodiments, the generator 408 can be a generative adversarial network(GAN). An exemplary neural network that can be included in the generator408 is described below in conjunction with FIG. 38. In some embodiments,the generator can include a neural network that can receive thebrightfield image 404 and output a single three-channel fluorescentimage (e.g., a 256×256×3 image). In some embodiments, the generator caninclude three neural networks that can each receive the brightfieldimage 404 and output a one-channel fluorescent image (e.g., a 256×256×1image). Generators that include three neural networks that can eachreceive the brightfield image 404 and output a one-channel fluorescentimage may be referred to as three-model generators. Each of the neuralnetworks can be trained to output a specific channel of fluorescence.For example, a first neural network can output a blue/all nuclei channelimage, a second neural network can output a green/apoptotic channelimage, and a third neural network can output a red/dead channel image.The flow 400 can include combining the blue/all nuclei channel image,the green/apoptotic channel image, and the red/dead channel image into asingle three-channel fluorescent image (e.g., a 256×256×3 image, a1024×1024×3 image, etc.).

The flow can include providing the brightfield image 404, the artificialfluorescent image 412, and a ground truth fluorescent image associatedwith brightfield image to a discriminator 416 that can predict whetheror not an image is real or generated by the generator 408 (e.g., theartificial fluorescent image 412). In some embodiments, the generator408 can receive an image and output a label ranging from 0 to 1, with 0indicating that image is generated by the generator 408 and 1 indicatingthat the image is real (e.g., the ground truth fluorescent imageassociated with the brightfield image 404). In some embodiments, thediscriminator 416 can be a PatchGAN discriminator, such as a 1×1PatchGAN discriminator. An exemplary discriminator is described below inconjunction with FIG. 39.

The flow 400 can include an objective function value calculation 420.The objective function value calculation 420 can include calculating anobjective function value based on labels output by the discriminator 416and/or by other metrics calculated based on the brightfield image 404,the artificial fluorescent image 412, and the ground truth fluorescentimage. The objective function value can capture multiple loss functions(e.g., a weighted sum of multiple loss functions). In this way, theobjective function value can act as a total loss value for the generator408 and the discriminator 416. The flow 400 can include transmitting theobjective function value and/or other information from the discriminator416 to the generator 408 and the discriminator 416 in order to updateboth the generator 408 and the discriminator 416. A number of differentsuitable objective functions can be used to calculate the objectivefunction value. However, in testing, a sum of GANLoss+0.83SSIM+0.17L1was shown to outperform other tested loss functions such as GANLoss+L1as used by the generator 408. GANLoss can be used to determine whetheran image is real or generated. The L1 loss can be used as an additionalobjective to be minimized to ensure that the generated and real imagehave the least mean absolute error in addition to GANLoss. StructuralSimilarity Index (SSIM) can be used to improve performance acrossmultiple performance metrics as well as reduce artifacts. The objectivefunction value calculation 420 will be described below.

The flow 400 can include receiving a number of pairs of a brightfieldimage and a corresponding ground truth fluorescence image, anditeratively training the generator 408 using each pair of images.

In some embodiments, the flow 400 can include pre-processing thebrightfield image 404 and the ground truth fluorescent image. Rawbrightfield and fluorescent images may have minimal contrast and requireenhancement before being used to train the generator 408. For example,in testing, the pixel intensities for the individual channels of thefluorescent image were generally skewed to zero, which may have beenbecause most of the image is black (i.e., background), except forregions containing organoids and/or cells.

In some embodiments, the artificial fluorescent image 412 can be used toprovide a count of live/dead cells. In order to enhance the contrast ofthe artificial fluorescent image 412 and improve the ability to countlive/dead cells from the artificial fluorescent image 412, both thebrightfield image 404 and the corresponding ground truth image canundergo contrast enhancement to brighten and sharpen organoids/cells.

In some embodiments, multiple brightfield images and multiple groundtruth fluorescent images can be generated per well. For example, for a96-well plate, there can be about 9-16 sites per well that get imaged.

In some embodiments, the raw brightfield and ground truth fluorescentimages can have pixel intensities ranging from [0, 2¹⁶]. First, acontrast enhancement process, which can be included in the organoidimage analysis application 132, can convert each image to an unsignedbyte format, with values ranging from [0, 255]. Next, the contrastenhancement process can stretch and clip each pixel intensity to adesired output range.

In some embodiments, the desired intensity range of the input to bestretched can be decided on a per image basis as follows: For the threepixel intensities corresponding to the three fluorophores used togenerate the fluorescent image, the input range can be re-scaled usingthe mode of the pixel intensity distribution as the lower bound valueand 1/10th the maximum pixel intensity as the upper bound. The contrastenhancement process can choose the upper bound in order to avoidoversaturated pixels and focus on cell signal. The contrast enhancementprocess can normalize each pixel intensity based on the lower bound andthe upper bound, which function as a min/max range, using a min-maxnorm, and then each pixel can be multiplied by the output range [0,255].For the brightfield image 404, the contrast enhancement process candetermine an input range by uniformly stretching the 2nd and 98thpercentile of pixel intensities to the output range [0,255].

For images with low signal, background noise may be included in theoutput range. To minimize any remaining back-ground noise, the contrastenhancement process can clip the minimum pixel value by two integervalues for the red and green channels, and by three integer values forthe blue channel, where the intensity range is wider on average. Themaximum pixel values can be increased accordingly to preserve intensityrange per image.

In some embodiments, the discriminator 416 can output a predicted label(e.g., a “0” or a “1”) to the objective function calculation 420. Thepredicted label can indicate if the artificial fluorescent image 412 isfake or real. In some embodiments, the objective function can becalculated as a weighted sum of GANLoss, SSIM, and L1. In someembodiments, the GANLoss can be calculated based on the predicted labeloutput by the discriminator. The GANLoss can be used to determinewhether the artificial fluorescent image 412 is real or generated. Insome embodiments, the L1 loss can be calculated based on the artificialfluorescent image 412 and the corresponding ground truth image. The L1loss can be used as an additional objective to be minimized to ensurethat the artificial fluorescent image 412 and the corresponding groundtruth image have the least mean absolute error in addition to GANLoss.

Certain machine learning models, such as the pix2pix model, may only useGANLoss and L1 loss in training a generator. As mentioned above, theobjective function calculation 420 can include an SSIM metric inaddition to the GANLoss and the L1 loss, which can improve theperformance of the generator 408 in comparison to a generator trainedusing only GANLoss and L1 loss.

In some embodiments, the objective function implemented in the objectivefunction calculation can be defined as:

$\begin{matrix}{G^{*} = {{\arg\underset{G}{\;\min\;}{\max\limits_{D}{\mathcal{L}_{GAN}\left( {G,D} \right)}}} + {\lambda\;{\mathcal{L}_{L1}(G)}} + {\beta\left( {1 - {\mathcal{L}_{SSIM}(G)}} \right)}}} & (1)\end{matrix}$where λ+β=1, L_(L1) is the mean absolute error loss, and 1−L_(SSIM)(G)is the structural similarity index loss between the generated image G(e.g., the fluorescent image 412) and the corresponding ground truthimage. In some embodiments, λ can be 0.17 and β can be 0.83. In someembodiments, λ can be selected from 0.1 to 0.3, and β can be selectedfrom 0.7 to 0.9.

In some embodiments, SSIM can take into account the luminance (1),contrast (c), and structure (s) of two images and computes a metricbetween 0 and 1, where 1 indicates a perfect match between the twoimages:

$\begin{matrix}{{l\left( {x,y} \right)} = \frac{{2\;\mu_{x}\mu_{y}} + C_{1}}{\mu_{x}^{2} + \mu_{y}^{2} + C_{1}}} & (2) \\{{c\left( {x,y} \right)} = \frac{{2\;\sigma_{x}\sigma_{y}} + C_{2}}{\sigma_{x}^{2} + \sigma_{y}^{2} + C_{2}}} & (3) \\{{s\left( {x,y} \right)} = \frac{\sigma_{xy} + C_{3}}{{\sigma_{x}\sigma_{y}} + C_{3}}} & (4)\end{matrix}$

C₁, C₂ and C₃ are small constants defined by:C ₁=(K ₁ L)² ,C ₂=(K ₂ L)² and C ₃ =C ₂/2  (5)where K₁, K₂ are two scalar constants whose values are less than 1, andL is the dynamic range of the pixel intensities (i.e. 256). SSIM canthen be calculated as:

$\begin{matrix}{{{SSIM}\left( {x,y} \right)} = {\left\lbrack {l\left( {x,y} \right)} \right\rbrack^{\alpha} \cdot \left\lbrack {c\left( {x,y} \right)} \right\rbrack^{\beta} \cdot \left\lbrack {s\left( {x,y} \right)} \right\rbrack^{\gamma}}} & (6) \\{{{SSIM}\left( {x,y} \right)} = \frac{\left( {{2\;\mu_{x}\mu_{y}} + C_{1}} \right)\left( {{2\;\sigma_{xy}} + C_{2}} \right)}{\left( {\mu_{x}^{2} + \mu_{y}^{2} + C_{1}} \right)\left( {\sigma_{x}^{2} + \sigma_{y}^{2} + {C\; 2}} \right)}} & (7)\end{matrix}$where l, c, and s are computed using the mean, variance and covariancerespectively of two images of the same size using a fixed window size.α, β, and γ are constants set to 1. In addition to structuralsimilarity, we also evaluated model prediction using root mean squareerror, which is the sum of the squared difference of pixel intensities.

In some embodiments, once a dye is added to a cell culture well, thecells in that well cannot continue to be used for the experiment, suchthat it is difficult or impossible to measure cell death in that well ata subsequent point in time. In some embodiments, the flow 400 caninclude generating artificial fluorescent images, which can reduce timerequirements for imaging by a factor of ten in comparison to utilizingdyes to generate the fluorescent images. Standard fluorescent imagingmay take up to an hour to perform. In some embodiments, the flow 400 canbe used in conjunction with a drug screening platform that uniquelyinterprets tumor organoids (TOs) which have limited biomass andintra-tumoral clonal heterogeneity by incorporating Patient DerivedTumor Organoids. The platform couples high content fluorescent confocalimaging analysis with a robust statistical analytical approach tomeasure hundreds of discrete data points of TO viability from as few as10{circumflex over ( )}3 cells.

Referring to FIG. 36 as well as FIG. 37, an exemplary flow 500 forgenerating an artificial fluorescent image 512 is shown. The flow 500can include providing an input brightfield image 504 of plated cells toa trained model 508. The trained model 508 can include the generator408, which can be trained using the flow 400. The trained model 508 canoutput an artificial fluorescent image 512. The fluorescent image 512can be used to generate a live/dead assays readout and/or analyze theeffectiveness of different drugs and/or dosages on cancer cells intissue organoids.

Notably, the flow 500 can produce the fluorescent image 512 without theuse of fluorescent dyes, which provides several advantages overtraditional fluorescent imaging processes that require the use offluorescent dyes. Some dyes have cytotoxicity and must be added acertain amount of time before imaging. Additionally, once certain dyesare added to a cell culture well, the cells in that well cannot continueto be used for reimaging because of the difficulty in measuring celldeath in that well at a subsequent point in time. Thus, the flow 500 canimprove the ease of generating the fluorescent images because the flow500 may only require brightfield imaging, which is not time-dependentlike the traditional fluorescent imaging. Additionally, the flow 500 canincrease the speed at which the fluorescent images are obtained, becausefluorescent dyes do not need to be applied to the cells, and because theflow 500 does not have to wait for the fluorescent dyes to diffusebefore imaging the cells. As another example, the flow 500 can allowmultiple fluorescent images to be generated for each cell well at anumber of different time points. The fluorescent dyes used intraditional fluorescent imaging can damage the cells enough to preventreimaging. In contrast, the flow 500 can be used to produce multiplefluorescent images over a time period of days, weeks, months, etc. Thus,the flow 500 can provide more data points per cell well than traditionalfluorescent imaging.

FIG. 38 shows an exemplary neural network 600. The neural network 600can be trained to receive an input image 604 and generate an artificialfluorescent image 608 based on the input image 604. In some embodiments,the input image 604 can be a raw brightfield image that has beenprocessed to enhance contrast and/or modify other characteristics inorder to enhance the raw brightfield image and potentially produce abetter artificial fluorescent image (e.g., the fluorescent image 608).

In some embodiments, the neural network 600 can include a Unetarchitecture. In some embodiments, the Unet architecture can be sized toreceive a 256×256×3 input image. The 256×256×3 input image can be abrightfield image. In some embodiments, the generator 408 in FIG. 36and/or the trained model 508 in FIG. 37 can include the neural network600.

FIG. 39 shows an exemplary discriminator 700. In some embodiments, thediscriminator 700 in FIG. 39 can be included as the discriminator 416 inthe flow 400 shown in FIG. 36. In some embodiments, the discriminator700 can be a 1×1 PatchGAN. In some embodiments, the discriminator 700can receive a brightfield image 704 and a fluorescent image 708. Thefluorescent image can be an artificial fluorescent image (e.g., thefluorescent image 608 in FIG. 38) or a ground truth fluorescent image.In some embodiments, each of the brightfield image 704 and thefluorescent image 708 can be 256×256×3 input images. In someembodiments, the brightfield image 704 and the fluorescent image 708 canbe concatenated. In some embodiments, the concatenated image can be a256×256×6 input image.

In some embodiments, the discriminator 700 can receive the brightfieldimage 704 and a fluorescent image 708 and generate a predicted label 712indicative of whether or not the fluorescent image 708 is real or fake.In some embodiments, the predicted label 712 can be a “0” to indicatethe fluorescent image 708 is fake, and “1” to indicate the fluorescentimage 708 is real. In some embodiments, the discriminator 700 caninclude a neural network

Referring to FIGS. 36-39, in some embodiments, the flow 400, the flow500, the neural network 600, and the discriminator 700 can beimplemented using Pytorch version 1.0.0. In some embodiments, the flow400 can be used to train the generator 408 to generate artificialfluorescent images for a colon cancer organoid line. In someembodiments, the flow 400 can be used to train the generator 408 togenerate artificial fluorescent images for a gastric cancer organoidline.

FIG. 40 shows an exemplary process 800 that can train a model togenerate an artificial fluorescent stain image of one or more organoidsbased on an input brightfield image. In some embodiments, the model canbe the generator 408 in FIG. 36, and/or the neural network 600. In someembodiments, the model can include a neural network that can receive theinput brightfield image and output a single three-channel fluorescentimage (e.g., a 256×256×3 image). In some embodiments, the model caninclude three neural networks that can each receive the brightfieldimage and output a one-channel fluorescent image (e.g., a 256×256×1image). The one-channel images can then be combined into a singlethree-channel fluorescent image.

In some embodiments, the process 800 can be used to train a model tooutput artificial fluorescent images of objects other than tumororganoids using a number of non-fluorescent images (e.g., brightfieldimages) and fluorescent stain images (which may have more or less thanthree channels) as training data.

The process 800 can be implemented as computer readable instructions onone or more memories or other non-transitory computer readable media,and executed by one or more processors in communication with the one ormore memories or other media. In some embodiments, the process 800 canbe implemented as computer readable instructions on the memory 220and/or the memory 240 and executed by the processor 204 and/or theprocessor 224.

At 804, the process 800 can receive training data. In some embodiments,the training data can include a number of brightfield images and anumber of associated real fluorescent images of organoids. In someembodiments, the organoids can be from a single tumor organoid line. Insome embodiments, the brightfield images and the real fluorescent imagescan be preprocessed in order to enhance contrast as described above. Insome embodiments, the brightfield images and the real fluorescent imagescan be raw images that have not undergone any preprocessing such ascontrast enhancement.

At 808, if the training data includes raw brightfield images and/or rawreal fluorescent images (i.e., “YES” at 808), the process 800 canproceed to 812. If the training data does not include any rawbrightfield images or raw real fluorescent images (i.e., “NO” at 808),the process 800 can proceed to 816.

At 812, the process 800 can preprocess at least a portion of thebrightfield images and/or real fluorescent images. In some embodiments,at 812, the process 800 can enhance the contrast of any raw brightfieldimages and/or real fluorescent images included in the training data. Insome embodiments, the raw brightfield and ground truth fluorescentimages can have pixel intensities ranging from [0, 2¹⁶]. In someembodiments, the process 800 can convert each image to an unsigned byteformat, with values ranging from [0, 255]. The process 800 can thenstretch and clip each pixel intensity to a desired output range.

In some embodiments, the process 800 can stretch the desired intensityrange of the input on a per image basis. For the three pixel intensitiescorresponding to the three fluorophores used to generate a realfluorescent image, the process 800 can re-scale the input range usingthe mode of the pixel intensity distribution as the lower bound valueand 1/10th the maximum pixel intensity as the upper bound. The process800 can determine the upper bound in order to avoid oversaturated pixelsand focus on cell signal. The process 800 can normalize each pixelintensity based on the lower bound and the upper bound, which functionas a min/max range, using a min-max norm, and then each pixel can bemultiplied by the output range [0,255]. For each brightfield imageincluded in the training data, the process 800 can determine an inputrange by uniformly stretching the 2nd and 98th percentile of pixelintensities to the output range [0,255].

For images with low signal, background noise may be included in theoutput range. In some embodiments, to minimize any remaining backgroundnoise, the process 800 can clip the minimum pixel value by two integervalues for the red and green channels, and by three integer values forthe blue channel, where the intensity range is wider on average. In someembodiments, the process 800 can increase maximum pixel valuesaccordingly to preserve intensity range per image.

At 816, the process 800 can provide a brightfield image to the model. Asdescribed above, in some embodiments, the model can be the generator 408in FIG. 36 and/or the neural network 600 in FIG. 38. In someembodiments, the model can include three neural networks, and eachneural network can receive a copy of the brightfield image and output adifferent channel (e.g., red, green, or blue) of an artificialfluorescent image.

At 820, the process 800 can receive an artificial fluorescent image fromthe model. The model can generate the artificial fluorescent image(e.g., the artificial fluorescent image 412) based on the brightfieldimage (e.g., the brightfield image 404) provided to the model. In someembodiments, the process 800 can receive three one-channel images fromthree neural networks included in the model and combine the one-channelimages into a single three-channel artificial fluorescent image.

At 824, the process 800 can calculate an objective function value basedon the brightfield image, the real fluorescent image associated with thebrightfield image, and the artificial fluorescent image. In someembodiments, the process 800 can determine a predicted label indicativeof whether or not the artificial fluorescent image is real or not byproviding the artificial fluorescent image and the real fluorescentimage to a discriminator (e.g., the discriminator 416). In someembodiments, the objective function value can be calculated usingequation (1) above, where λ is 0.17 and β is 0.83. In some embodiments,λ can be selected from 0.1 to 0.3, and β can be selected from 0.7 to0.9. In some embodiments, the learning rate can fixed at 0.0002 for afirst number of epochs (e.g., fifteen epochs) of training, and thenlinearly decayed to zero over a second number of epochs (e.g., tenepochs).

At 828, the process 800 can update the model (e.g., the generator 408)and the discriminator (e.g., the discriminator 416) based on theobjective function value. In some embodiments, the model and thediscriminator can each include a neural network. In some embodiments,the process 800 can update weights of layers included in neural networksincluded in the model and the discriminator based on the objectivefunction value.

At 832, the process 800 can determine whether or not there is abrightfield image included in the training data that has not beenprovided to the model. If there is a brightfield image included in thetraining data that has not been provided to the model (e.g., “YES” at832), the process can proceed to 816 in order to provide the brightfieldimage to the model. If there are no brightfield images included in thetraining data that has not been provided to the model (e.g., “NO” at832), the process can proceed to 836.

At 836, the process 800 can cause the model to be output. At 836, themodel has been trained, and can be referred to as a trained model. Insome embodiments, the process 800 can cause the trained model to beoutput to at least one of a memory (e.g., the memory 220 and/or thememory 240) and/or a database (e.g., the trained models database 128).The trained model may be accessed and used in certain processes, such asthe processes in FIGS. 41 and 45. The process 800 can then end.

FIG. 41 shows an exemplary process 900 that can generate an artificialfluorescent image of one or more organoids based on a brightfield image.More specifically, the process 900 can generate the artificialfluorescent image using a trained model. In some embodiments, the modelcan be the generator 408 in FIG. 36, the trained model 508, and/or theneural network 600 in FIG. 38 trained using the process 800. In someembodiments, the model can include a neural network that can receive theinput brightfield image and output a single three-channel fluorescentimage (e.g., a 256×256×3 image). In some embodiments, the model caninclude three neural networks that can each receive the brightfieldimage and output a one-channel fluorescent image (e.g., a 256×256×1image). The one-channel images can then be combined into a singlethree-channel fluorescent image.

In some embodiments, the process 900 can be used to generate artificialfluorescent images (which can have one channel, two channels, threechannels, etc.) of objects other than tumor organoids using anon-fluorescent image (e.g., a brightfield image). In this way, objectsother than tumor organoids that require fluorescent staining to beproperly imaged can be artificially generated without the use of and/ordrawbacks of fluorescent dyes.

The process 900 can be implemented as computer readable instructions onone or more memories or other non-transitory computer readable media,and executed by one or more processors in communication with the one ormore memories or other media. In some embodiments, the process 900 canbe implemented as computer readable instructions on the memory 220and/or the memory 240 and executed by the processor 204 and/or theprocessor 224.

At 904, the process 900 can receive a brightfield image (e.g., thebrightfield image 404 in FIG. 36 and/or the brightfield image 504 inFIG. 37) of one or more organoids. In some embodiments, the brightfieldimage can be preprocessed in order to enhance contrast as describedabove. In some embodiments, the brightfield image can be a raw imagethat has not undergone any preprocessing such as contrast enhancement.

At 908, the process 900 can determine if the brightfield image isunprocessed (i.e., raw). If the brightfield image is unprocessed (i.e.,“YES” at 908), the process 900 can proceed to 912. If the brightfieldimage is not unprocessed (i.e., “NO” at 908), the process 900 canproceed to 916.

At 912, the process 900 can preprocess the brightfield image. In someembodiments, the brightfield image can have pixel intensities rangingfrom [0, 2¹⁶]. In some embodiments, the process 900 can convert thebrightfield image to an unsigned byte format, with values ranging from[0, 255]. In some embodiments, the process 900 can convert thebrightfield image to another format with less bits than the originalpixel intensity. The process 900 can then stretch and clip each pixelintensity to a desired output range. In some embodiments, the process900 can determine an input range for the brightfield image by uniformlystretching the 2nd and 98th percentile of pixel intensities in thebrightfield image to an output range [0,255].

At 916, the process 900 can provide the brightfield image to a trainedmodel. In some embodiments, the model can include the generator 408 inFIG. 36 trained using the process 800 in FIG. 40, the trained model 508,and/or the neural network 600 trained using the process 800 in FIG. 40.In some embodiments, the trained model can include three neuralnetworks, and each neural network can receive a copy of the brightfieldimage and output a different channel (e.g., red, green, or blue) of anartificial fluorescent image.

At 920, the process 900 can receive an artificial fluorescent image fromthe trained model. In some embodiments, the process 900 can receivethree one-channel images from three neural networks included in thetrained model and combine the one-channel images into a singlethree-channel artificial fluorescent image. The artificial fluorescentimage can indicate whether cells included in the tumor organoids arealive or dead.

At 924, the process 900 can cause the artificial fluorescent image to beoutput. In some embodiments, the process 900 can cause the artificialfluorescent image to be output to at least one of a memory (e.g., thememory 220 and/or the memory 240) and/or a display (e.g., the display116, the display 208, and/or the display 228). The artificialfluorescent image can be used to provide a live/dead count of cells inthe organoids. In some embodiments, the process 900 can cause theartificial fluorescent image to be output to an automatic cell countingprocess in order to receive an accurate live/dead count of cells and/ora cell count report in the artificial fluorescent image. For example,the process 900 can cause the artificial fluorescent image to be outputto the CellProfiler available at https://cellprofiler.org.

At 924, the process 900 can cause the artificial fluorescent image to beoutput. In some embodiments, the process 900 can cause the artificialfluorescent image to be output to at least one of a memory (e.g., thememory 220 and/or the memory 240) and/or a display (e.g., the display116, the display 208, and/or the display 228). The artificialfluorescent image can be used to provide a live/dead count of cells inthe organoids. In some embodiments, the process 900 can cause theartificial fluorescent image to be output to an automatic cell countingprocess in order to receive an accurate live/dead count of cells, apercentage of cells that are viable (alive) or dead, and/or a cell countreport in the artificial fluorescent image. For example, the process 900can cause the artificial fluorescent image to be output to theCellProfiler available at https://cellprofiler.org. In some embodiments,the process 900 can cause one or more channels of the artificialfluorescent image to be output to an automatic cell counting process inorder to receive a cell count report, a percentage of cells that areviable (alive) or dead, and/or accurate live/dead count of cells in theartificial fluorescent image. In some embodiments, the process 900 cancause the brightfield image to be output to a trained model in order toreceive a cell count report, a percentage of cells that are viable(alive) or dead, and/or accurate live/dead count of cells in theartificial fluorescent image. In some embodiments, the process 900 cancause a combination (e.g., image embeddings combined by concatenation)of the brightfield image and one, two, or three channels of theartificial fluorescent image to be output to an automatic cell countingprocess in order to receive a cell count report, a percentage of cellsthat are viable (alive) or dead, and/or an accurate live/dead count ofcells in the artificial fluorescent image.

In some embodiments, at 924, the process 900 can identify cells in theartificial fluorescent image by converting each of the channels tograyscale, enhancing and suppressing certain features such as speckles,ring shapes, neurites, dark holes, identifying primary objects belongingto the all cell channel where the typical diameters of these objects (inpixel units) is set anywhere between 2 and 20 with a minimum crossentropy thresholding method at a smoothing scale of 1.3488, andidentifying primary objects again belonging to the dead cells channelwhere typical diameter is anywhere between 5 and 20 in pixel units. Inthis way, the process 900 can generate a cell count report. In someembodiments, the process 924 can determine if a drug and/or dosage iseffective in killing tumor organoid cells based on the live/dead countof cells. In some embodiments, at 924, the process 900 can extrapolatedose response from a distribution of organoid viability at a singleconcentration.

In some embodiments, the cell count report may be analyzed to quantifythe efficacy of the drug in killing a particular line of tumor organoidcells. For example, if a concentration of a drug causes a lower numberof live cells and/or greater number of dead cells, the drug may be ratedas more effective in killing a particular line of tumor organoid cells.For each line of tumor organoid cells, characteristics of the tumororganoid cells (for example, molecular data including detectedmutations, RNA expression profiles measured in the tumor organoid cellsetc. and/or clinical data associated with the patient from which thetumor organoid was derived) and the results (including the drug efficacyrating) of each drug dose may be saved in a database of drug assayresults. These results may be used to match therapies to patients. Forexample, if a patient has a cancer with characteristics similar to atumor organoid cell line, drugs rated as effective in killing thosetumor organoid cells may be matched to the patient.

In some embodiments, the process 900 can generate a report based on thecell count, the cell count report, and/or the artificial fluorescentimage. In some embodiments, the process 900 can cause the report to beoutput to at least one of a memory (e.g., the memory 220 and/or thememory 240) and/or a display (e.g., the display 116, the display 208,and/or the display 228). The process 900 can then end.

FIG. 42 shows exemplary raw images before preprocessing and afterpreprocessing. The raw images before preprocessing include a brightfieldimage 1004, a blue/all nuclei channel fluorescent image 1008, agreen/apoptotic channel fluorescent image 1012, red/pink/dead channelfluorescent image 1016, and a combined 3-channel fluorescent image 1020.The preprocessed images include a brightfield image 1024, a blue/allnuclei channel fluorescent image 1028, a green/apoptotic channelfluorescent image 1032, red/pink/dead channel fluorescent image 1036,and a combined 3-channel fluorescent image 1040. The organoids and cellsare brighter and sharper in the preprocessed images. In someembodiments, the preprocessed images 1024-1040 can be generated at 812in the process 800 in FIG. 40.

FIG. 43 shows an exemplary flow 1100 for culturing tumor organoids.Culture of patient derived tumor organoids. The flow 100 can includeobtaining tumor tissue from a same-day surgery, disassociating cellsfrom the tumor tissue, and culturing the tumor organoids from the cells.An example of systems and methods for culturing tumor organoids may befound in U.S. patent application Ser. No. 16/693,117, titled “TumorOrganoid Culture Compositions, Systems, and Methods” and filed Nov. 22,2019, which is incorporated by reference herein in its entirety. Tumortissue sent from hospitals is cultured to form tumor organoids.

FIG. 44 shows an exemplary flow 1200 for conducting drug screens inaccordance with systems and methods described herein. In someembodiments, the flow 1200 can include disassociating tumor organoidsinto single cells, plating the cells (e.g., in a well plate such as a96-well plate and/or a 384-well plate), growing the cells into organoidsover a predetermined time period (e.g., seventy-two hours), treating theorganoids with at least one therapeutic technique, and imaging the tumororganoids a predetermined amount of time (e.g., seventy-two hours) afterthe tumor organoids are treated. In some embodiments, only brightfieldimaging may be performed on the tumor organoids, and any brightfieldimages generated can be used to generate artificial fluorescent imagesusing the process 900 in FIG. 41. A live/dead count can then begenerated based on the artificial fluorescent images. One example ofsystems and methods for using tumor organoids for drug screens may befound in PCT/US20/63619, titled “Systems and Methods for PredictingTherapeutic Sensitivity” and filed Oct. 22, 1920, which is incorporatedby reference herein in its entirety.

FIG. 45 shows an exemplary process 1300 that can generate artificialfluorescent images at multiple time points for at least one organoid.Notably, the process 1300 can provide an advantage over standardfluorescent imaging techniques. As mentioned above, fluorescent dyesused to generate standard fluorescent images can damage the cells (e.g.,killing the cells) in the organoids, and do not permit fluorescentimages to be generated at different time points (e.g., every twelvehours, every twenty-four hours, every seventy-two hours, every week,etc.). In contrast, the process 1300 permits repeated fluorescentimaging of organoids because the process 1300 may only requirebrightfield images (which do not damage the organoids), and can generateartificial fluorescent images based on the brightfield images.

The process 1300 can be implemented as computer readable instructions onone or more memories or other non-transitory computer readable media,and executed by one or more processors in communication with the one ormore memories or other media. In some embodiments, the process 1300 canbe implemented as computer readable instructions on the memory 220and/or the memory 240 and executed by the processor 204 and/or theprocessor 224.

At 1304, the process 1300 can receive an indication to analyze treatedorganoids at multiple time points. In some embodiments, the organoidscan be plated (e.g., in a well plate such as a 96-well plate and/or a384-well plate). In some embodiments, the organoids can be plated onmultiple well plates. In some embodiments, the organoids can be platedon one or more petri dishes. In some embodiments, the organoids can betreated using a variety of different treatments, which can vary in drugtype, drug concentration, and/or other parameters. In some embodiments,each well in a well plate can be associated with a different treatment.

In some embodiments, the multiple time points can represent a time afterthe organoids have been treated. For example, a twelve hour time pointcan be twelve hours after the time at which the organoids were treated.In some embodiments, the multiple time points can be spaced at regularintervals. For example, the multiple time points can occur every twelvehours, every twenty-four hours, every seventy-two hours, every week,etc. In some embodiments, the multiple time points can be irregularlyspaced. For example, the time points can include a first time point atsix hours, a second time point at twenty four-hours, a third time pointat three days, a fourth time point at one week, and a fifth time pointat twenty-eight days.

At 1308, the process 1300 can wait until the next time point included inthe multiple time points. For example, if six hours has passed since theorganoids have been treated, and the next time point is at twelve hours,the process 1300 can wait for six hours.

At 1312, the process 1300 can cause at least one brightfield image ofthe treated organoids to be generated. In some embodiments, process 1300can generate the brightfield images of the treated organoids using abright-field microscope and generating fluorescent images of the cellsusing a confocal microscope such as a confocal laser scanningmicroscope. In some embodiments, the process 1300 can preprocess the atleast one brightfield image. For example, the process 1300 can, for eachbrightfield image, perform at least a portion of 912 in the process 900in FIG. 41. In some embodiments, multiple brightfield images can begenerated for each well. For example, for a 96-well plate, there can beabout 9-16 sites per well that get imaged.

At 1316, the process 1300 can cause at least one artificial fluorescentimage to be generated based on the at least one brightfield image. Insome embodiments, the process 1300 can provide each brightfield image toa trained model, and receive an artificial fluorescent image associatedwith the brightfield image from the trained model. In some embodiments,the trained model can include the generator 408 in FIG. 36 trained usingthe process 800 in FIG. 36, the trained model 508, and/or the neuralnetwork 600 trained using the process 800 in FIG. 36. In someembodiments, the trained model can include a neural network that canreceive the input brightfield image and output a single three-channelfluorescent image (e.g., a 256×256×3 image).

In some embodiments, the trained model can include three neural networksthat can each receive the brightfield image and output a one-channelfluorescent image (e.g., a 256×256×1 image). The one-channel images canthen be combined into a single three-channel fluorescent image. The atleast one artificial fluorescent image can indicate whether cellsincluded in the tumor organoids are alive or dead.

At 1320, the process 1300 can cause the at least one fluorescent imageto be output. In some embodiments, the process 1300 can cause the atleast one artificial fluorescent image to be output to at least one of amemory (e.g., the memory 220 and/or the memory 240) and/or a display(e.g., the display 116, the display 208, and/or the display 228). The atleast artificial fluorescent image can be used to provide a live/deadcount of cells in the organoids. In some embodiments, the process 900can cause the artificial fluorescent image to be output to an automaticcell counting process in order to get an accurate live/dead count ofcells in the artificial fluorescent image. For example, the process 900can cause the artificial fluorescent image to be output to theCellProfiler available at https://cellprofiler.org. In this way, theprocess 1300 can automatically generate live/dead counts for multiplewells at multiple time points, which can make drug treatment experimentsrun faster and gather more data with the same number of wells ascompared to standard fluorescent dye imaging techniques that kill cells.

In some embodiments, at 1320, the process 1300 can identify cells in theartificial fluorescent image by converting each of the channels tograyscale, enhancing and suppressing certain features such as speckles,ring shapes, neurites, dark holes, identifying primary objects belongingto the all cell channel where the typical diameters of these objects (inpixel units) is set anywhere between 2 and 20 with a minimum crossentropy thresholding method at a smoothing scale of 1.3488, andidentifying primary objects again belonging to the dead cells channelwhere typical diameter is anywhere between 5 and 20 in pixel units. Inthis way, the process 1300 can generate a cell count report. In someembodiments, the process 1300 can generate a report based on the cellcount, the cell count report, and/or the artificial fluorescent image.In some embodiments, the process 1300 can cause the report to be outputto at least one of a memory (e.g., the memory 220 and/or the memory 240)and/or a display (e.g., the display 116, the display 208, and/or thedisplay 228). The process 1300 can then end.

In some embodiments, the process 800 in FIG. 40, the process 900 in FIG.41, and/or the process 1300 in FIG. 45 can be included in the organoidsimage analysis application 132 in FIG. 33.

EXAMPLES A. Example 1—PARPi Sensitivity of BRCA1⁻/BRCA2⁻ Tumors

Tumor biopsies were collected from four individuals: UK1393,10423-12001, 10524-12001, and 10941-12002. The tumors were genotyped anddetermined not carry BRCA1 or BRCA2 mutations. As such, according toconventional guidance, the tumors are not indicated for first-linetherapy with a PARP inhibitor. In order to test whether these tumors maystill be sensitive to PARP inhibitors, despite not carrying a BRCA1 orBRCA2 mutation, tumor organoids were cultured using cells from thebiopsied material.

Briefly, cells within each of the biopsied tumor samples weredissociated and cultured to establish a tumor organoid culture line fromthe tumor of each individual. Single tumor organoids from the tumororganoid culture lines were collected and plated, individually, intowells of a multiwell plate with culture medium. A series of sixconcentrations of olaparib, in a 5-fold serial dilution, wereestablished for the tumor organoids derived from each individual, aswell as negative controls. After incubation with olaparib, a caspase 3/7apoptosis assay was performed in each well, to determine the sensitivityof each tumor organoid to the respective concentration of olaparib.Results of the caspase dilution series experiments are plotted in FIG.3.

As expected, since the tumors did not carry a BRCA1 or BRCA2 mutation,three of the tumor organoid culture lines were fairly insensitive toolaparib: 10423-12001 (FIG. 3B), 10524-12001 (FIG. 3C), and 10941-12002(FIG. 3D). However, unexpectedly, tumor organoid culture line UK1393 wasmore than 100-fold more sensitive to olaparib, despite being derivedfrom a tumor that did not carry a BRCA1 or BRCA2 mutation. These resultssuggest that the cancer in this individual may be effectively treatedwith a PARP inhibitor as a first-line therapy, despite not carrying abiomarker conventionally associated with PARP inhibitor efficacy.

B. Example 2—Drug Screening Platform

Patient derived Tumor Organoids (TOs) are emerging aspatient-representative models that recapitulate clinical responses tocandidate therapeutics. Yet standard methods of interpreting in vitrotreatments of TOs have been developed from monoclonal, rapidlyproliferating 2D cell lines that are not amenable for TOs which havelimited biomass and intra-tumoral clonal heterogeneity. To address thesechallenges, a drug screening platform more applicable for the uniquecharacteristics of TOs was developed and optimized. The platform coupleshigh content fluorescent confocal imaging analysis with a robuststatistical analytical approach to measure hundreds of discrete datapoints of TO viability from as few as 10{circumflex over ( )}3 cells.This approach was validated through evaluating responses to hundreds ofsmall molecule inhibitors as well as a panel of chemotherapeutic agentsin TO models derived from different patients.

The platform was highly reproducible with minimal intra- and inter-assayvariance (well:well variance=ns, plate:plate variance=ns, by ANOVA). QCof TOs was performed to remove outlier TOs by size and remaining TOswere normalized by mean vehicle proportion survival. To comparedifferential therapeutic toxicity between TOs from different patients, alinear model was developed to evaluate differences in proportion ofsurviving cells across equivalent therapeutic concentrations,identifying highly significant differences (P<10⁻¹²). Intriguingly, thelinear model not only uncovered heterogeneity of responses between TOsderived from different patients, but also identified organoid clonalpopulations derived from the same patient with differential drugresponse offering a window into uncovering functional intratumoralheterogeneity.

Lastly, throughput was substantially increased by applying a machinelearning algorithm to predict therapeutic response via TO morphologicalchanges from light microscopy. Employing this algorithm eliminated theneed for fluorescent labeling leading to increased assay throughput by3-4 fold amounting to 96 and 384 well plate acquisitions in as little as5 and 15 minutes respectively. As such provided herein is ahigh-throughput capable of measuring TO therapeutic response with highstatistical confidence and exquisite inter-assay reproducibility. Thisapproach can be utilized in research settings to elucidate heterogeneityof therapeutic responses within and among patients, and may be utilizedin the clinical laboratory to potentially guide precision oncologytreatments.

1. Cell Plating

A drug screening platform may be utilized to assess the effectiveness ofdrugs or other agents on tumor organoids (TOs). Single cell suspensionsof tumor organoid cells are generated as described below.

-   -   1. Gently remove the medium from the tumor organoids (TOs) by        pipetting out the medium. Do not use vacuum to aspirate the        medium.    -   2. Add 500 mL of TrypLE Express to each well, attempting to        disrupt the organoid containing Matrigel domes.    -   3. Scrape the remaining Matrigel off the bottom of the wells        with 1 mL pipette.    -   4. Place the dissociated organoids into a 15 ml low-binding        centrifuge tube.    -   5. Place the tube in the 37° C. water bath for 15 min.    -   6. Spin the cells at 200 g for 5 min. in a 4° C. centrifuge.    -   7. Carefully pipet out and discard the supernatant.    -   8. Add 1 ml of DPBS to the cell pellet.    -   9. Centrifuge the cell containing tube at 4° C. at 200 g for 5        min.    -   10. Pipet out the supernatant and add any medium/reagent        required for your following procedure.

In one example, TOs from 24-well plate culture are dissociated to singlecells and seeded in 384-well plates in a mix of 30% Matrigel and 70%media. This allows TOs to form from individual cells for the assay,maintaining TO heterogeneity in each well. The cells are seeded at 2,000cells per well allowing a sufficient number of TOs to form while notovercrowding the plate so that TOs do not overlap or touch allowing foreasy identification of individual TOs.

2. Drug and Detection Reagent Application

Caspase 3/7 Green Apoptosis Assay Reagent (Essen Biosciences cat #4440)is diluted in media to 2.5 μM. Drugs are diluted to 20 μM in media withCaspase 3/7 reagent and 10-fold serial dilutions are prepared in aseparate 384-well plate. Diluted drugs are added to the 384-well assayplate by pipetting 20 μl of the diluted drug, media, Caspase 3/7 mix tothe appropriate wells using an Integra Viaflo automated pipettingsystem.

In one example, one or more chemotherapeutic agents may be applied tothe TOs in the well plates. Example agents include Paclitaxel,Gemcitabine, Cisplatin, Carboplatin, Oxaliplatin, Capecitabine, SN-38(CPT-11), 5-FU, MTX (methotrexate), Docetaxel, Bortezomib, Everolimus,Ulixertinib, Dasatinib, Vinblastine, Nelarabine, Epirubicin, Afatinib,Lapatinib, Cytarabine, Cladribine, Doxorubicin, Azacitidine, andStaurosporine. Other examples include classes of drugs including but notlimited to: taxanes, platinating agents, vinca alkaloids, alkylatingagents, and anthracyclines.

C. Example 3—High Content Fluorescent Confocal Imaging Analysis

TOs may be stained using common vital dyes to measure cellular behaviorsamenable for high content fluorescent confocal imaging analysis. In oneexample, TOs are stained with Hoechst 33342 (Fisher Scientific cat#H3570), IncuCyte® Caspase-3/7 Green Apoptosis Assay Reagent (EssenBiosciences cat #4440), and TO-PRO™-3 Iodide (642/661) (FisherScientific cat #T3605) in multi-well tissue culture plates (e.g. 24, 48,96, 384, etc.). After staining, TOs are imaged on an inverted confocalmicroscope using the light microscopy and multiple fluorescent channelswith varying wave-length excitation sources (e.g. laser or LED) andemission filters. Each channel (i.e. light microscopy, Hoechst nuclearstain, FITC, Cy5) is acquired through an objective lens, in one examplea 10× objective lens is used to take images at two sites per well with astack of images in the Z plane ranging from 1-100 heights in the Z-planewith increments ranging from submicron to as high as 15 micron perZ-plane height. Z-stack images are projected to 2D and analyzed usingimage analysis software with parameters to identify TOs based on thepixel intensities in the nuclear stain channel (i.e. Hoechst 33342channel) and the size of the object by measurements in 2D space of theobject as well as number of nuclei. All cell nuclei are identified byHoechst 33342 staining. TOs are identified by clusters of nucleiidentified by Hoechst 33342 staining. Apoptotic cells are identified byCaspase 3/7 staining and dead cells are identified by TO-PRO-3 stainingoverlapping with Hoechst 33342 staining. The analysis module is used toenumerate TOs per image, how many cells (live and dead) are present ineach TO, and how many dead and dying cells are present in each TO byCaspase 3/7 and TO-PRO-3 staining separately. The Caspase 3/7 andTO-PRO-3 stains provide two independent counts of dead and dying cells.

Utilizing fluorescent markers for all cells and two markers fordead/apoptotic cells permits analysis of TOs at the single cell leveland also permits generation of an absolute number of live and dead cellsper organoid. This TO by TO analysis provides more information thansimply calculating a relative value of viable cells from an entire well.Maintaining TO heterogeneity allows for determination of whether allcells are dying at a constant rate or if there is a mix of susceptibleand resistant cells to a given treatment based on the distribution ofviable cells per organoid. The aspects, such as the number of wells,types of plates, and types of cultures disclosed here, are exemplary innature. Other aspects known in the art may be used instead or incombination with those aspects disclosed herein.

FIG. 4 shows an example image of high-content fluorescent confocalimaging analysis. (A-D) Brightfield (gray scale), Hoechst 33342 (blue),Caspase 3/7 (green), and TO-PRO-3 (red) staining is shown for vehiclecontrol (A and B) and staurosporine treated (C and D) gastric cancertumor organoids. (B and D) Overlays of the fluorescent channels and theresult of image analysis are shown side by side. Light-blue coloring onthe image analysis indicates the area of a given tumor organoid. Live(purple) and dead (red) cells are shown in the image analysis panels.Scale bars represent 100 microns.

D. Example 4—Statistical Approach

Since data are collected from hundreds of TOs per drug condition,hundreds of data points may be analyzed within an experimental replicate(usually a single culture well in a multiwell plate), rather than justcollecting one data point from each experimental replicate well as istypically done in drug screens. The percent viable cells (which may bedetermined by a lack of Caspase 3/7 and/or TO-PRO-3 staining) iscalculated for each individual TO. The percent viable cells per organoidfrom each technical replicate is then averaged together for the actualpercent viable cells per drug concentration. These values are normalizedto the percent viable cells per TO of the DMSO treated vehicle controland plotted to generate a dose-response curve.

Table 3 contains a small example of the data output. Each row of thetable represents an individual organoid. The columns contain informationfor which well of the culture plate was imaged (Well Name), the size ofthe TO (TO area), the total number of cells in the organoid (All cells(TO-PRO-3) and All cells (Caspase 3/7)), the number of dead cells bypositive TO-PRO-3 staining (Dead cells (TO-PRO-3)), the number of livecells by negative TO-PRO-3 staining (Live cells (TO-PRO-3)), the numberof dead cells by positive Caspase 3/7 staining (Dead cells (Caspase3/7)), and the number of live cells by negative Caspase 3/7 staining(Live cells (Caspase 3/7)).

TABLE 3 All Live Dead All Live Dead cells cells cells cells cells cells(TO- (TO- (TO- (Cas- (Cas- (Cas- Well PRO- PRO- PRO- pase pase pase NameTO Area 3) 3) 3) 3/7) 3/7) 3/7) O02 410.178223 2 1 1 2 2 0 O02152.455612 1 0 1 1 0 1 O02 319.430817 1 0 1 1 0 1 O02 3570.00244 0 0 0 00 0 O02 212.348892 1 0 1 1 0 1 O02 246.832901 1 0 1 1 0 1 O02 348.4699711 0 1 1 0 1 O02 2268.68481 7 0 7 7 0 7 O02 132.491196 1 0 1 1 0 1 O021969.21838 7 1 6 7 0 7 O02 1069.00427 4 1 3 4 1 3 O02 373.879242 1 0 1 10 1 O02 176.049942 1 0 1 1 0 1 O02 1386.62012 4 0 4 4 0 4 O02 214.1638491 0 1 1 0 1 O02 397.473572 1 0 1 1 0 1 O02 343.025146 1 0 1 1 0 1 O02346.655029 1 0 1 1 0 1 O02 840.320862 2 1 1 2 1 1 O02 208.718994 1 0 1 10 1 O02 264.982391 1 0 1 1 0 1 O02 464.626648 1 0 1 1 0 1 O02 947.4027712 0 2 4 0 4 O02 446.477173 1 0 1 1 0 1 O02 568.078674 3 0 3 3 0 3 O02431.957581 2 0 2 2 0 2 O02 713.274475 2 0 2 2 0 2 O02 110.711815 1 0 1 10 1 O02 980.071838 3 1 2 3 0 3

FIG. 5 shows an example dose-response curve for cell viability byTO-PRO-3 (left) and Caspase 3/7 (right) after treatment withstaurosporine on tumor organoids derived from a gastric cancer. Eachpoint represents the mean±SD from 450-1200 individual organoids.

Hundreds of discrete measurements of TO viability per drug concentrationcan be made, beginning with as few as 6×10⁵ cells. These measurementsmay be performed for every cell of every organoid. There are hundreds oforganoids per experimental condition such as incubation with a drug at aspecific concentration with approximately between 3-15 cells perorganoid.

Additionally, high reproducibility with minimal inter- and intra-assayvariance can be achieved using the subject platform.

Due to the large number of TOs being measured per well, there is a highnumber of sampling occurring to identify the true mean. Just as onearrives at a high precision of 0.1666666 probability of rolling a six ona standard die if one rolls the die 600 times instead of just 6 times.The high accuracy of finding the true mean means that interwellreproducibility is high and inter assay reproducibility is also high.

To compare differential therapeutic toxicity between TOs from differentpatients, a linear model was developed to evaluate survival acrossequivalent therapeutic concentrations.

The high number of measurements recorded per dose allows the use of morecomplex statistical or non-statistical methods that would otherwise beunable to be used with a low-throughput dose response assay. Therefore,a linear model was used (Galton, 1886, “Regression Towards Mediocrity inHereditary Stature,” The Journal of the Anthropological Institute ofGreat Britain and Ireland 15: 246-263) to determine differences betweenpatients, or between drugs, at equivalent therapeutic concentrations (ordoses). Use of a linear model allows for inclusion of covariates toadjust for potential confounding technical effects including initial TOviability, differences in growth rates between TOs derived fromdifferent patients, and different cancer types, and leverages all of theTO data to gain better statistical power.

For example, to determine differences in viability to a 10 uM dose ofstaurosporine compared to a 10 uM dose of Olaparib that were run ondifferent experiments (plates), we would run the following model andestimate the betas and corresponding p-values for each main effect(x_(drug), and x_(plate)). Since we estimate both the drug and plateeffect, we can control for potential experimental noise generated by theexperiment being run on different plates.Y _(normalized viability proportion) ˜βx _(drug) +βx _(plate)+ε

In a different example, differences in viability to a 1 nM dose ofDapagliflozin between TO cell lines that were run by differenttechnicians is determined. Here, differences between TO lines(estimating the beta for)(canine) and determined, with control forexperimental noise that might be due to different technicians runningthe assay:Y _(normalized viability proportion) ˜βx _(cell line) +βx_(technician)+ε

Lastly, throughput was substantially increased by applying a deeplearning algorithm to the high-dimensional dataset. The algorithm wasadapted from the Generative Adversarial Network Pix2pix by Phillip Isolaet al, 2018, “Image-to-Image Translation with Conditional AdversarialNetworks,” arXiv:1611.07004v3 [cs.CV] 26 Nov. 2018, which is herebyincorporated by reference. The GAN architecture makes use of a generatormodel that is trained to generate meaningful data (typically fromnoise), and a discriminator model that is trained to distinguish betweenwhat's real and what's been generated.

Pix2pix is a specific type of GAN designed for general purposeImage-to-Image translation involving the controlled conversion of asource image to target image. Here, pix2pix was employed to transfer thestyle of brightfield images to fluorescent images.

FIG. 6 shows two examples of predictions on a test set where thebrightfield image is shown in the far left column, the model generatedfluorescent image is shown in the middle column, and is the actualfluorescent image is shown in the far right column. As shown in FIG. 6,the trained model accurately predicted morphological responses topharmacological agents based on brightfield images, thus potentiallyreducing a 384 well plate reading to as little as several minutes.

In summary, the systems and methods described here may measuretherapeutic response in patient derived tumor organoids with highstatistical confidence and technical reproducibility. The approach maybe utilized in both research and clinical settings to better understandheterogeneity of therapeutic responses within and among patients, andfurther guide precision oncologic decision-making.

E. Example 5—Example PARP Inhibitor Drug Screen

A recent development in precision oncology is the use of poly(ADP-ribose) polymerase (PARP) inhibitors in patients whose tumorsexhibit evidence of homologous recombination deficiency (HRD),especially in cases of somatic loss of BRCA1 or BRCA2, or LOH offunctional BRCA1/2 alleles in patients with inherited non-functionalBRCA1/2 alleles.

To advance TO utility for drug development and precision medicine, auniversal label-free TO drug screening assay was developed. Withoutsacrificing throughput, this neural network prediction of drug responsefrom light microscopy achieved high reproducibility compared topreviously described metabolic-based assays (Tiriac et al., CancerDiscov 8, 1112-1129 (2018); Tiriac et al., Isolation andCharacterization of Patient-derived Pancreatic Ductal AdenocarcinomaOrganoid Models. J Vis Exp. (2020); Vlachogiannis et al., Science 359,920-926 (2018)). Importantly, the network identified clinically relevantdrug responses across a broad range of cancer-types including PARPiresponse from HRD-positive and -negative TOs. Notably, the PARPiresponses in this small cohort of organoids were generally higher inHRD-positive TOs, with certain drugs exhibiting more potency in vitrothan others. These observations confirm previous studies in patients(Coleman et al., Lancet 390, 1949-1961 (2017)) and short-term ovariancancer organoid cultures (Hill et al., Cancer Discov 8, 1404-1421(2018)). In addition, the RCA network identified response to afatinibassociated with a high ERBB2 (HER2) amplification in the gastric TO. Thedistribution of response among the TO populations was demonstrative offunctional intratumoral heterogeneity, as evidenced by a subset of theorganoid population exhibiting resistance to afatinib. When correlatedwith the patient's clinical course, we found that the biopsy used toestablish this TO line was obtained during disease progression onanti-ERBB2 therapy. Identifying heterogeneity in drug response has thepotential to uncover mechanisms of primary resistance to noveltherapeutics and may also provide predictive and prognostic informationfor treatment response in personalized clinical assays

To provide proof-of-concept for this precision medicine approach, weevaluated ten TO lines (breast, endometrial, colon, ovarian, and NSCLC)based on HRD status. We assessed genome-wide LOH and categorized TOs aseither HRD-positive or HRD-negative (Timms et al., Breast CancerResearch 16, 475 (2014); Yi et al., International Journal of Cancer 145,1209-1220 (2019)). We then exposed TOs to a panel of FDA-approved PARPinhibitors and compared predicted drug responses, based on a trainedneural network, to fluorescent-based readouts. We found the networkpredictions were strongly correlated with fluorescent-based drugresponses across all cancer types (FIG. 7). Additionally, theclassifier's measurement of PARPi response was able to discriminatebetween HRD-positive and HRD-negative organoids, comparable tofluorescent-based measurements (FIG. 8). Thus, the network-based assayaccurately predicts clinically relevant drug responses.

1. Methodology

Tumor specimens were received from multiple institutions and processedfor organoid culture at a CLIA-certified, CAP-accredited laboratory. Allspecimens were collected from consented patients under protocolsmonitored by the Institutional Review Board at each institution. Thespecimens were placed into iced RPMI (Lifetech) and processed the sameday or after overnight storage at 4° C. The specimens were then digestedto single-cell suspension with a GentleMACs instrument using theMiltenyi Tumor Dissociation kit (Miltenyi Biotec) according to themanufacturer's protocol.

2. Development and Culture of Tumor Organoids

Tumor organoids (TOs) were developed using an adapted epithelialcell-only submerged Matrigel culture technique (Sato et al., Nature 459,262-265 (2009)). Briefly, cells were seeded at a density of 400 cells/μlin a 50 μl droplet of GFR Matrigel matrix (Corning) and grown in24-well, flat-bottom tissue culture plates (Eppendorf). Growth mediumwas changed twice weekly and TOs were passaged every 1-2 weeks asneeded. For passaging, growth media was removed, and Matrigel domes weredisrupted mechanically by adding 500 μl TrypLE Express Enzyme (GIBCO)per well. TOs were transferred to 15-ml conical tubes and incubated in a37° C. water bath for 10 minutes. Cells were then centrifuged at 200×gfor 3 minutes and resuspended in 2 ml DPBS. Cells were resuspended inGFR Matrigel Matrix at 400 cells per microliter, and 50 μl domes wereplated in pre-warmed, 24-well, flat-bottom plates and left to polymerizein a 37° C. incubator before the addition of 500 μl growth medium and 10μM Y-27632 (Bio-techne) per well.

TO cultures were maintained in serum-free defined media conditions basedon the consensus of previous reports for anticipated tumor types;namely, Advanced DMEM/F12 nutrient mix supplemented with B27,nicotinamide, n-acetylcysteine, recombinant growth factors EGF, Noggin,R-spondin1 (RSPO-1), Wnt-3A, FGF-2, 7, and 10, and small moleculeinhibitors of p38 MAPK and TGF-beta.

3. Tumor Organoid Growth Analysis

Two independent observers determined organoid formation and serialpassaging. Cells that demonstrated exponential increase in biomass onserial passage were deemed high proliferation and were cryopreserved inRecovery Cell Culture Freezing media (Gibco) when biomass reached >10⁶cells, following the manufacturer's protocol.

For quantitative organoid growth measurements, bright-field images of24-well plates containing TOs were captured at a minimum of twodifferent time points for the same passage using the ImageXpress MicroConfocal high-content imaging system (Molecular Devices, CA). Theaverage total area sum of TOs was then plotted by time and the maximumgrowth rate was calculated by linear regression.

4. Pathologic Evaluation

Cultures were intermittently selected for formalin fixation and wereprepared for H&E staining either via paraffin embedding or cyto-spin.H&E stains of organoid cultures from 320 patients were interpreted by aboard-certified pathologist (GK) using histologic and cytologicfeatures. Due to the use of growth factors, cultures displayed featuresof cellular proliferation including nuclear hyperchromasia with slightpleomorphism, increase in nuclear to cytoplasmic ratio and conspicuousnucleoli. Mitotic figures were frequent with few atypical forms. Assuch, the identification of malignant cells on the basis of cytologicfeatures was obscured and was therefore based on the degree of thesechanges and the presence of marked loss of polarity and macronucleoli.

5. Sample Processing and Nucleic Acid Isolation

Organoids were dissociated using TrypLE Express Enzyme (GIBCO) anddissociated cells were immediately lysed in 350 μL of buffer RLT fromthe Allprep DNA/RNA Micro Kit (Qiagen) and stored at −80° C. Uponthawing, cell lysates were homogenized using QIAshredder spin columns(Qiagen). DNA and RNA were isolated using the Allprep DNA/RNA Micro Kitper manufacturer's instructions.

HLA-typed PBMC controls used for flow cytometry analysis werecommercially procured as cryovials. PBMCs were thawed, washed, andresuspended in RPMI growth medium containing 10% FBS and 2 mML-glutamine. Cells were left to recover for 1 hr at 37° C.

6. Next-Generation Sequencing

Next-generation sequencing was conducted as previously described(Beaubier et al., 2019a; Beaubier et al., 2019b). Briefly, nucleic acidswere library prepped using a targeted gene panel as previouslydescribed, loaded on an Illumina HiSeq 4000 (Illumina), and DNA tumorlibraries were sequenced to an average unique on-target depth of 500×.Similarly, RNA was prepared using an exome-capture RNA-Seq protocol andsequenced on the Illumina HiSeq 4000 platform. Variant calls and othermolecular signatures, such as copy-number alterations, fusion events,and mRNA gene expression levels, were called and reported based onestablished guidelines.

7. Associations of Clinical and Molecular Features with SustainableGrowth

Clinical features including tissue site, T stage, N stage, M stage, race(caucasian, Asian, African-american, other), sex, neoadjuvant therapytreatment, and biospecimen features such as cell count and cold ischemiatime (>12 hours) were structured as binary values and evaluated as ageneralized linear model. Logistic regression was then applied to modelhigh-proliferation organoid growth as a linear combination of clinicalfeatures using the stats R package (v3.5.2).

Analysis of single-nucleotide variants (SNV), copy-number alterations(CNA), and transcriptome profiling was performed using a NGS assay(Beaubier et al., Nat Biotechnol 37, 1351-1360 (2019); Beaubier et al.,Oncotarget 10, 2384-2396 (2019)). The combined effect of pathogenicvariants within a gene classified by the assay platform were then testedusing a gene-based test as implemented in SKAT-o (Lee et al., Americanjournal of human genetics 91, 224-237 (2012)). Gene-based tests wereperformed within each cancer and combined across cancers. Transcriptomeanalysis was performed as described below.

Copy-number calls were determined for paired probes by an internalalgorithm that considered tumor purity and sequencing depth in atwo-pass approach. First, segmentation of chromosomal regions wasperformed to define chromosomal regions with specific copy numbers usingpanel coverage data and transformed to provide probe-level copy-numbervalues. Next, the segment data was used in a second algorithm toapproximate integer copy-number values for major (total observed copiesat a given loci) and minor allele count as well as estimates for tumorpurity, ploidy, and B-allele frequencies (BAF). Following segmentation,copy number and tumor purity were assessed using a grid searchmethodology. Starting from the initial estimate of the tumor puritylower bound, a copy-number state matrix was generated containingtotal/minor copy state combinations and their expected log ratios andlog BAF given the initialized tumor purity. Each segment was projectedinto the matrix and the log probability that it belonged to eachanalyzed copy state was computed based on drawing from a normalprobability density function defined by the expected log-ratio and thepre-computed log ratio standard deviation. The same process was appliedfor log BAF with a sliding-weight scale based on the number ofheterozygous germline variants observed within the segment. This wasdone to account for noise in log BAF in the context of sparseobservations.

8. Drug Screen and Image Analysis

A 320-drug panel was purchased from Selleckchem, which is detailed ontheir website (found online atselleckchem.com/screening/selective-library.html). Additional compoundsare included in STAR methods.

TOs were dissociated as described above and resuspended in a 30:70% mixof GFR Matrigel:growth media at a concentration of 100 cells/μl. Thesolution was added to 384-well assay plates (Corning) at 20 μl per wellfor a final concentration of 2,000 cells per well. Assay plates werecovered with a Breathe-Easy sealing membrane (Sigma Aldrich) to preventevaporation. TOs were grown for 72 hours before drug addition. Drugswere prepared in growth media with 2.5 μM Caspase-3/7 Green ApoptosisAssay Reagent (Essen Bioscience). Serial dilutions of each molecule wereprepared in 384-well polystyrene plates (Nunc). Diluted drug was addedto the assay plate using an Integra Viaflo pipette (Integra) mounted onan Integra Assist Plus Pipetting Robot (Integra). Assay plates wereagain covered with a Breathe-Easy sealing membrane and TOs were exposedto drugs for another 72 hours before imaging.

Prior to imaging, TOs were incubated with 4 μM Hoechst 33342 (FisherScientific) and 300 nM TO-PRO-3 Iodide (642/661) (Invitrogen) for 1.5-2hours. Assay plates were imaged using an ImageXpress Micro Confocal(Molecular Devices) at 10× magnification so that ˜100-200 TOs wereimaged per well. Images were acquired as 4×15 μm Z-stacks and the 2Dprojections were analyzed to assess cell viability. Confocal images wereanalyzed using the MetaXpress software (Molecular Devices) custom moduleeditor feature to design an analysis module that identified TOs byclusters of Hoechst 33342 staining, individual cells by Hoechst 33342staining, and dead/dying cells by either TO-PRO-3 or Caspase-3/7staining. The result of this analysis module is a spreadsheet detailingthe number of live and dead cells for every individual organoid.

The percentage of viable cells per organoid was calculated based on theimage analysis described above. Organoids with fewer than three cellsand larger than the top one percent by size were excluded from analysis.The mean viability for all organoids at a given drug concentration wasused in dose-response curves to calculate AUC. AUC was calculated usingthe computeAUC function using settings for “actual” AUC of the R PackagePharmacoGx (v1.17.1). Heatmaps of AUC values were generated using thePheatmap package (v1.0.12) in R. Scatterplots of AUC values weregenerated using the ggplot2 package (v3.3.0) in R.

9. Response and Label-Free Prediction from Brightfield Images

The multiplexed fluorescence images were 1024×1024×3 RGB images, wherered corresponds to dead cells (TO-PRO-3), green to apoptotic cells(Caspase-3/7), and blue to nuclei (Hoechst 33342). All wavelengthchannels underwent a simple intensity rescaling contrast enhancementtechnique to brighten and sharpen the TOs/cells as well as removebackground noise. The mean viability for all organoids per site wasobtained from the MetaXpress software readout.

F. Example 6—PARPi Sensitivity in Colorectal Cancer Tumor Organoids

In this example, PARPi sensitivity was measured for two colorectal tumororganoids (TOs). Each TO or source tissue associated with the TO (forexample, a patient biopsy used to generate the TO) were geneticallysequenced to determine whether the TO had variants (mutations) inhomologous recombination proteins (including BRCA1 and BRCA2) andwhether the TO was likely to have homologous recombination deficiency(HRD), according to an HRD engine.

None of the TOs had pathogenic single nucleotide variants (SNVs) inBRCA1 or BRCA2. None of the TOs had loss of heterozygosity (LOH) ofBRCA1 or BRCA2. None of the TOs had bi-allelic loss of BRCA1 or BRCA2(both a pathogenic SNV and LOH) and traditionally would not have beenexpected to have HRD or respond to PARPi therapy. BRCA mutation and LOHstatus was based on sequencing analysis of either the TO or the sourcetissue.

Regardless of the lack of bi-allelic BRCA loss, some of the TOs wereanalyzed by an HRD engine and predicted to have HRD based on othercriteria.

The HRD engine is described in U.S. patent application Ser. No.16/789,363, titled “An Integrated Machine-Learning Framework To PredictHomologous Recombination Deficiency”, filed Feb. 12, 2020, the contentsof which are incorporated by reference herein in their entirety for anyand all purposes. In another embodiment, the HRD score or likelihood ofPARPi sensitivity may be determined by the trained classifier disclosedherein.

One of the TOs had a low HRD score (unlikely to have HRD or be sensitiveto PARPi) and one of the TOs had a high HRD score (likely to have HRDand be sensitive to PARPi).

All of the TOs were grown in culture wells and each well was exposed for96 hours to either a negative control (1% DMSO) or one of multiple PARPitherapies (Rucaparib, Niraparib, Pamiparib, Talazoparib, Olaparib, orVeliparib) at one of three concentrations (1 nM, 100 nM, or 10,000 nM).Some of the organoids were exposed to additional concentrations as notedin the x-axis of the figures.

At 96 hours the cells were stained by either caspase 3/7 or TO-PRO-3(caspase 3/7 stained dying, apoptic cells and TO-PRO-3 stained deadcells). For each PARPi therapy, efficacy was measured at eachconcentration by detecting the proportion of viable cells that were notstained and normalizing that proportion to the proportion of viablecells in the negative control well, where the proportion of negativecontrol well cells that were viable was adjusted to 100% or 75%(indicated in the figure). This normalization resulted in someexperimental wells having a proportion of viable cells that was greaterthan 100% (or 75%). A best fit curve was generated for the viabilityproportion at each concentration and an inverse AUC was calculated asthe area between actual cell viability (the best fit curve) and 100% (or75%) cell viability. The inverse AUC served as an additional measure ofPARPi efficacy, where a higher inverse AUC indicated a higher drugefficacy.

The PARPi therapies were hypothesized to be more effective against TOswith a high HRD score than TOs with a low HRD score.

The viability data shown below indicate that the PARPi therapies weremore effective against TOs with a high HRD score than TOs with a low HRDscore.

For organoids that were PARPi sensitive, PARPi therapies were effectiveagainst TOs that did not have BRCA1 or BRCA2 mutations or biallelicinactivation of BRCA1 or BRCA2.

FIGS. 9A and 9B illustrate viability data for a tumor organoid having alow HRD score, a pathogenic BRCA1 mutation, no BRCA2 mutations and noBRCA1 or BRCA2 LOH. The x-axis label indicates the PARP-i therapy anddose. The γ-axis indicates the percent of cells in the well that wereviable, normalized to the DMSO mock condition (negative control).

FIGS. 10A and 10B illustrate viability data for a colorectal tumororganoid having a high HRD score, no BRCA1 or BRCA2 mutations and noBRCA1 or BRCA2 LOH. The x-axis label indicates the PARP-i therapy anddose. The γ-axis indicates the percent of cells in the well that wereviable, normalized to the DMSO mock condition (negative control).

G. Example 7—PARPi Sensitivity in Breast Cancer Tumor Organoids

In this example, PARPi sensitivity was measured for three tumororganoids (TOs). Each TO or source tissue associated with the TO (forexample, a patient biopsy used to generate the TO) were geneticallysequenced to determine whether the TO had variants (mutations) inhomologous recombination proteins (including BRCA1 and BRCA2) andwhether the TO was likely to have homologous recombination deficiency(HRD), according to an HRD engine.

None of the TOs had pathogenic single nucleotide variants (SNVs) inBRCA1 or BRCA2. None of the TOs had loss of heterozygosity (LOH) ofBRCA1 or BRCA2. None of the TOs had bi-allelic loss of BRCA1 or BRCA2(both a pathogenic SNV and LOH) and traditionally would not have beenexpected to have HRD or respond to PARPi therapy. BRCA mutation and LOHstatus was based on sequencing analysis of either the TO or the sourcetissue.

Regardless of the lack of bi-allelic BRCA loss, some of the TOs wereanalyzed by an HRD engine and predicted to have HRD based on othercriteria.

The HRD engine is described in U.S. patent application Ser. No.16/789,363, titled “An Integrated Machine-Learning Framework To PredictHomologous Recombination Deficiency”, filed Feb. 12, 2020, the contentsof which are incorporated by reference herein in their entirety for anyand all purposes. In another embodiment, the HRD score or likelihood ofPARPi sensitivity may be determined by the trained classifier disclosedherein.

Two of the TOs had a low HRD score (unlikely to have HRD or be sensitiveto PARPi) and one of the TOs had a high HRD score (likely to have HRDand be sensitive to PARPi).

All of the TOs were grown in culture wells and each well was exposed for96 hours to either a negative control (1% DMSO) or one of multiple PARPitherapies (Rucaparib, Niraparib, Pamiparib, Talazoparib, Olaparib, orVeliparib) at one of three concentrations (1 nM, 100 nM, or 10,000 nM).Some of the organoids were exposed to additional concentrations as notedin the x-axis of the figures.

At 96 hours the cells were stained by either caspase 3/7 or TO-PRO-3.Caspase 3/7 stained dying, apoptic cells and TO-PRO-3 stained deadcells. For each PARPi therapy, efficacy was measured at eachconcentration by detecting the proportion of viable cells that were notstained and normalizing that proportion to the proportion of viablecells in the negative control well, where the proportion of negativecontrol well cells that were viable was adjusted to 100% or 75%(indicated in the figure). This normalization resulted in someexperimental wells having a proportion of viable cells that was greaterthan 100% (or 75%). A best fit curve was generated for the viabilityproportion at each concentration and an inverse AUC was calculated asthe area between actual cell viability (the best fit curve) and 100% (or75%) cell viability. The inverse AUC served as an additional measure ofPARPi efficacy, where a higher inverse AUC indicated a higher drugefficacy.

The PARPi therapies were hypothesized to be more effective against TOswith a high HRD score than TOs with a low HRD score.

The viability data shown below indicate that the PARPi therapies weremore effective against TOs with a high HRD score than TOs with a low HRDscore.

For organoids that were PARPi sensitive, PARPi therapies were effectiveagainst TOs that did not have BRCA1 or BRCA2 mutations or biallelicinactivation of BRCA1 or BRCA2.

FIGS. 11A and 11B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 12A and 12B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 13A and 13B illustrate viability data for a tumor organoid havinga high HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

H. Example 8—PARPi Sensitivity in Ovarian Cancer Tumor Organoids

In this example, PARPi sensitivity was measured for two ovarian cancertumor organoids (TOs). Each TO or source tissue associated with the TO(for example, a patient biopsy used to generate the TO) were geneticallysequenced to determine whether the TO had variants (mutations) inhomologous recombination proteins (including BRCA1 and BRCA2) andwhether the TO was likely to have homologous recombination deficiency(HRD), according to an HRD engine.

None of the TOs had pathogenic single nucleotide variants (SNVs) inBRCA1 or BRCA2. One of the TOs had loss of heterozygosity (LOH) of BRCA1or BRCA2 and had only one functioning copy of the gene. None of the TOshad bi-allelic loss of BRCA1 or BRCA2 (both a pathogenic SNV and LOH)and traditionally would not have been expected to have HRD or respond toPARPi therapy. BRCA mutation and LOH status was based on sequencinganalysis of either the TO or the source tissue.

Regardless of the lack of bi-allelic BRCA loss, some of the TOs wereanalyzed by an HRD engine and predicted to have HRD based on othercriteria.

The HRD engine is described in U.S. patent application Ser. No.16/789,363, titled “An Integrated Machine-Learning Framework To PredictHomologous Recombination Deficiency”, filed Feb. 12, 2020, the contentsof which are incorporated by reference herein in their entirety for anyand all purposes. In another embodiment, the HRD score or likelihood ofPARPi sensitivity may be determined by the trained classifier disclosedherein.

One of the TOs had a low HRD score (unlikely to have HRD or be sensitiveto PARPi) and one of the TOs had a high HRD score (likely to have HRDand be sensitive to PARPi).

All of the TOs were grown in culture wells and each well was exposed for96 hours to either a negative control (1% DMSO) or one of multiple PARPitherapies (Rucaparib, Niraparib, Pamiparib, Talazoparib, Olaparib, orVeliparib) at one of three concentrations (1 nM, 100 nM, or 10,000 nM).Some of the organoids were exposed to additional concentrations as notedin the x-axis of the figures.

At 96 hours the cells were stained by either caspase 3/7 or TO-PRO-3.Caspase 3/7 stained dying, apoptic cells and TO-PRO-3 stained deadcells. For each PARPi therapy, efficacy was measured at eachconcentration by detecting the proportion of viable cells that were notstained and normalizing that proportion to the proportion of viablecells in the negative control well, where the proportion of negativecontrol well cells that were viable was adjusted to 100% or 75%(indicated in the figure). This normalization resulted in someexperimental wells having a proportion of viable cells that was greaterthan 100% (or 75%). A best fit curve was generated for the viabilityproportion at each concentration and an inverse AUC was calculated asthe area between actual cell viability (the best fit curve) and 100% (or75%) cell viability. The inverse AUC served as an additional measure ofPARPi efficacy, where a higher inverse AUC indicated a higher drugefficacy.

The PARPi therapies were hypothesized to be more effective against TOswith a high HRD score than TOs with a low HRD score.

The viability data shown below indicate that the PARPi therapies weremore effective against TOs with a high HRD score than TOs with a low HRDscore.

For organoids that were PARPi sensitive, PARPi therapies were effectiveagainst TOs that did not have BRCA1 or BRCA2 mutations or biallelicinactivation of BRCA1 or BRCA2.

FIGS. 14A and 14B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations, BRCA1 LOH, and no BRCA2LOH. The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 15A and 15B illustrate viability data for a tumor organoid havinga high HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control). Lower doses of PARPi weremore effective for this organoid than the low HRD shown in the previousfigures organoid.

I. Example 9—PARPi Sensitivity in Non-Small Cell Lung Cancer CancerTumor Organoids

In this example, PARPi sensitivity was measured for two non-small celllung cancer tumor organoids (TOs). Each TO or source tissue associatedwith the TO (for example, a patient biopsy used to generate the TO) weregenetically sequenced to determine whether the TO had variants(mutations) in homologous recombination proteins (including BRCA1 andBRCA2) and whether the TO was likely to have homologous recombinationdeficiency (HRD), according to an HRD engine.

None of the TOs had pathogenic single nucleotide variants (SNVs) inBRCA1 or BRCA2. None of the TOs had loss of heterozygosity (LOH) ofBRCA1 or BRCA2. None of the TOs had bi-allelic loss of BRCA1 or BRCA2(both a pathogenic SNV and LOH) and traditionally would not have beenexpected to have HRD or respond to PARPi therapy. BRCA mutation and LOHstatus was based on sequencing analysis of either the TO or the sourcetissue.

Regardless of the lack of bi-allelic BRCA loss, some of the TOs wereanalyzed by an HRD engine and predicted to have HRD based on othercriteria.

The HRD engine is described in U.S. patent application Ser. No.16/789,363, titled “An Integrated Machine-Learning Framework To PredictHomologous Recombination Deficiency”, filed Feb. 12, 2020, the contentsof which are incorporated by reference herein in their entirety for anyand all purposes. In another embodiment, the HRD score or likelihood ofPARPi sensitivity may be determined by the trained classifier disclosedherein.

One of the TOs had a low HRD score (unlikely to have HRD or be sensitiveto PARPi) and one of the TOs had a high HRD score (likely to have HRDand be sensitive to PARPi).

All of the TOs were grown in culture wells and each well was exposed for96 hours to either a negative control (1% DMSO) or one of multiple PARPitherapies (Rucaparib, Niraparib, Pamiparib, Talazoparib, Olaparib, orVeliparib) at one of three concentrations (1 nM, 100 nM, or 10,000 nM).Some of the organoids were exposed to additional concentrations as notedin the x-axis of the figures.

At 96 hours the cells were stained by either caspase 3/7 or TO-PRO-3.Caspase 3/7 stained dying, apoptic cells and TO-PRO-3 stained deadcells. For each PARPi therapy, efficacy was measured at eachconcentration by detecting the proportion of viable cells that were notstained and normalizing that proportion to the proportion of viablecells in the negative control well, where the proportion of negativecontrol well cells that were viable was adjusted to 100% or 75%(indicated in the figure). This normalization resulted in someexperimental wells having a proportion of viable cells that was greaterthan 100% (or 75%). A best fit curve was generated for the viabilityproportion at each concentration and an inverse AUC was calculated asthe area between actual cell viability (the best fit curve) and 100% (or75%) cell viability. The inverse AUC served as an additional measure ofPARPi efficacy, where a higher inverse AUC indicated a higher drugefficacy.

The PARPi therapies were hypothesized to be more effective against TOswith a high HRD score than TOs with a low HRD score.

The viability data shown below indicate that the PARPi therapies weremore effective against TOs with a high HRD score than TOs with a low HRDscore.

For organoids that were PARPi sensitive, PARPi therapies were effectiveagainst TOs that did not have BRCA1 or BRCA2 mutations or biallelicinactivation of BRCA1 or BRCA2.

FIGS. 16A and 16B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

FIGS. 17A and 17B illustrate viability data for a tumor organoid havinga high HRD score, no BRCA1 or BRCA2 mutations, no BRCA1 LOH and havingBRCA2 LOH. The x-axis label indicates the PARP-i therapy and dose. Theγ-axis indicates the percent of cells in the well that were viable,normalized to the DMSO mock condition (negative control).

J. Example 10—PARPi Sensitivity in Endometrial Tumor Organoids

In this example, PARPi sensitivity was measured for one endometrialtumor organoid (TOs). Each TO or source tissue associated with the TO(for example, a patient biopsy used to generate the TO) were geneticallysequenced to determine whether the TO had variants (mutations) inhomologous recombination proteins (including BRCA1 and BRCA2) andwhether the TO was likely to have homologous recombination deficiency(HRD), according to an HRD engine.

None of the TOs had pathogenic single nucleotide variants (SNVs) inBRCA1 or BRCA2. None of the TOs had loss of heterozygosity (LOH) ofBRCA1 or BRCA2. None of the TOs had bi-allelic loss of BRCA1 or BRCA2(both a pathogenic SNV and LOH) and traditionally would not have beenexpected to have HRD or respond to PARPi therapy. BRCA mutation and LOHstatus was based on sequencing analysis of either the TO or the sourcetissue.

The TO was analyzed by an HRD engine and predicted not to have HRD.

The HRD engine is described in U.S. patent application Ser. No.16/789,363, titled “An Integrated Machine-Learning Framework To PredictHomologous Recombination Deficiency”, filed Feb. 12, 2020, the contentsof which are incorporated by reference herein in their entirety for anyand all purposes. In another embodiment, the HRD score or likelihood ofPARPi sensitivity may be determined by the trained classifier disclosedherein.

One of the TOs had a low HRD score (unlikely to have HRD or be sensitiveto PARPi).

All of the TOs were grown in culture wells and each well was exposed for96 hours to either a negative control (1% DMSO) or one of multiple PARPitherapies (Rucaparib, Niraparib, Pamiparib, Talazoparib, Olaparib, orVeliparib) at one of three concentrations (1 nM, 100 nM, or 10,000 nM).Some of the organoids were exposed to additional concentrations as notedin the x-axis of the figures.

At 96 hours the cells were stained by either caspase 3/7 or TO-PRO-3.Caspase 3/7 stained dying, apoptic cells and TO-PRO-3 stained deadcells. For each PARPi therapy, efficacy was measured at eachconcentration by detecting the proportion of viable cells that were notstained and normalizing that proportion to the proportion of viablecells in the negative control well, where the proportion of negativecontrol well cells that were viable was adjusted to 100% or 75%(indicated in the figure). This normalization resulted in someexperimental wells having a proportion of viable cells that was greaterthan 100% (or 75%). A best fit curve was generated for the viabilityproportion at each concentration and an inverse AUC was calculated asthe area between actual cell viability (the best fit curve) and 100% (or75%) cell viability. The inverse AUC served as an additional measure ofPARPi efficacy, where a higher inverse AUC indicated a higher drugefficacy.

The viability data shown below indicate that the PARPi therapies werenot effective against a TO with a low HRD score.

FIGS. 18A and 18B illustrate viability data for a tumor organoid havinga low HRD score, no BRCA1 or BRCA2 mutations and no BRCA1 or BRCA2 LOH.The x-axis label indicates the PARP-i therapy and dose. The γ-axisindicates the percent of cells in the well that were viable, normalizedto the DMSO mock condition (negative control).

K. Example 11—A Pan-Cancer Organoid Platform for Precision Medicine

1. Summary

Patient-derived tumor organoids are emerging as high-fidelity models tostudy cancer biology and develop novel precision medicine therapeutics.However, utilizing organoid technologies for systems biology-basedapproaches has been limited by a lack of scalable and reproduciblemethods to develop and profile these models. The generation of a robustpan-cancer organoid platform with chemically defined media was describedand its evaluation in cultures from over one thousand patients.Crucially, tumor genetic and transcriptomic recapitulation wasdemonstrated utilizing this approach, and further optimize definedminimal media for organoid initiation and propagation. Additionally, aneural network-based approach for label-free, light microscopy baseddrug-assays capable of predicting patient-specific heterogeneity in drugresponses and with universal applicability across solid cancers wasdeveloped. The pan-cancer platform, molecular data, and neuralnetwork-based drug assay serve as a resource to accelerate the broadimplementation of organoid models for systems biology-based precisionmedicine research and guide the development of personalized therapeuticprofiling programs.

2. Introduction

Over the past decade, oncology therapy has moved from“one-size-fits-all” to a more individualized treatment approach known asprecision medicine. A core objective of precision medicine is theidentification of therapies that target the unique biology of eachpatient's disease. For example, targeted therapies have been designedagainst multiple single molecular alterations, such as mutations indriver oncogenes (Bailey et al., 2018; Cancer Genome Atlas, 2012a, b;Cancer Genome Atlas Research, 2012) gene fusions (Seshagiri et al.,2012), and protein overexpression (Ancevski Hunter et al., 2018;Sanchez-Vega et al., 2019), and can elicit deep and durable responses.However, patients harboring clinically actionable alterations showvarying responses to therapies targeting those alterations, potentiallydue to differential cellular states or genetic backgrounds. A criticalbarrier to the widespread adoption of precision medicine has been thelack of a robust and reproducible preclinical platform to connectmolecular profiles of patient tumors with effective therapeutictreatments in a scalable manner suited for systems biology approaches

Recently, patient-derived tumor organoid (TO) technologies have beenused to create cellular models of diverse cancer types, including colon(Fujii et al., 2016; van de Wetering et al., 2015), breast (Sachs etal., 2018), pancreatic (Boj et al., 2015; Romero-Calvo et al., 2019;Tiriac et al., 2018), liver (Broutier et al., 2017), lung (Sachs et al.,2019), endometrial (Boretto et al., 2017; Turco et al., 2017), prostate(Gao et al., 2014), and esophagogastric (Kijima et al., 2019; Li et al.,2018; Nanki et al., 2018), among others. In addition to advancingfundamental research, TOs have recently been employed for drugdevelopment and precision medicine studies. For example, several groupshave reported that patient-derived organoid cultures mimic patientresponses to chemotherapies (de Witte et al., 2020; Driehuis et al.,2019; Ferguson et al., 2020; Narasimhan et al., 2020; Ooft et al., 2019)or chemoradiation (Ganesh et al., 2019; Yao et al., 2020). However,these protocols lack consistency within and between specific tumors andreproducibility is limited owing to utilization of undefined cell-lineconditioned media and therapeutic profiling assays have not demonstratedpan-cancer generalizability.

Here, a pan-cancer platform was established for utilizing TOs in drugscreening and modeling responses to cancer therapies in a large-scaleproduction pipeline. This study included analysis of over 1,000 organoidcultures representing high-incidence cancers to assess clinical andmolecular determinants of organoid culture success, molecularconcordance between TOs and source tumors, minimal growth factordependencies by tissue of origin, and development of a label-free neuralnetwork-based therapeutic profiling assay applicable across all cancertypes. Taken together, the combination of these resources establishesrobust and reproducible standards for the application of TOs for systemsbiology approaches to unlock new precision oncology strategies.

3. Results

a. Surveying Tumor Organoid Production at Scale

A patient-derived TOs was developed from a large variety of cancers foruse in precision medicine and high-throughput translational researchapplications, including biobanking, molecular profiling, and drugscreening (FIG. 19A). TO cultures from 1,298 patient tumors representingthe major incidences of carcinoma in the US were initiated. To maximizeconsistency, organoids were cultured and passaged as single-cellsuspensions in extracellular matrix (ECM, Matrigel) using chemicallydefined culture media representing a consensus of previous reports (FIG.19E, STAR methods). Of the 1,298 cultures, 213 were excluded from thisanalysis due to atypical, benign or non-malignant diagnoses. Of the 1085remaining TO cultures, 73% (792/1085) established organoids asvisualized by light microscopy, which is consistent with previousreports (Boj et al., 2015; Boretto et al., 2019; Broutier et al., 2017;Fujii et al., 2016; Gao et al., 2014; Li et al., 2018; Nanki et al.,2018; Sachs et al., 2018; Sachs et al., 2019; Tiriac et al., 2018;Tiriac et al., 2020; Turco et al., 2017; van de Wetering et al., 2015).Intermittent histological evaluation (n=320, FIG. 19B) by aboard-certified pathologist identified rare instances of benignepithelia in 5% (16/320) of cultures.

Growth in TOs generated was further evaluated from prevalent histologictumor subtypes with at least 10 unique cultures (FIG. 19C). It wasdetermined that 15-60% of TO cultures (depending on cancer type)exhibited a sustainable high proliferation phenotype. These highproliferation TO cultures were able to expand to 10⁶-10⁷ cells uponserial passage, and were suitable for biobanking for future researchstudies (FIG. 19C). Additionally, time-lapse microscopy on a subset ofTOs (n=165) revealed that cultures exhibited heterogeneity of growthwithin and between cancer types (FIG. 19D). The maximal observed growthrates were between 10 and 1,000 μm²/day (interquartile ranges), withcolon and gastric adenocarcinomas exhibiting the largest proportion ofrapidly growing TO types (FIG. 19D).

b. Clinical and Molecular Associations of Tumor Organoid Proliferation

The ability to predict which TO cultures will achieve high proliferationwill enable researchers to quickly identify TOs that can be biobankedversus those that are more suitable for a single, patient-specificstudy. Clinical and molecular features enriched in high and low/noproliferation TOs were examined using a generalized linear model withhistologic subtype as a covariate (FIG. 19E). Initial counts of >100,000viable cells positively contributed to the high-proliferation phenotype(P=0.012), and within histologic subtypes was significant in NSCLCadenocarcinoma and breast invasive ductal carcinoma (IDC) (P=0.039 andP=0.0012, respectively; hypergeometric test). Interestingly, clinicalfeatures such as neoadjuvant treatment, lymph node metastasis, and tumorsize were not associated with a proliferation phenotype, with theexception of gastric and rectal adenocarcinoma. In these cases, TOsdeveloped from metastatic lesions were significantly associated withhigh proliferation (P=0.021 and P=0.026 respectively; hypergeometrictest).

To test for molecular signatures in source tumors that predicthigh-proliferation TOs, 125 source tumors (74% with high proliferation)were identified with available data from the Tempus xT test, a targetedDNA sequencing panel of 596 oncogenes. Enrichment of pathogenicmolecular alterations were tested for within each of the 596 genessequenced with the Tempus xT panel (Beaubier et al., 2019a; Beaubier etal., 2019b), but found no significant single-gene mutation differences.Thus, based on these 125 examples, this result indicates no overtproliferation bias toward TOs with specific genetic drivers using thepan-cancer approach described herein.

c. Tumor Organoids Recapitulate Genomic and Transcriptomic Profiles ofSource Tumors

To verify whether TO cultures recapitulated patient molecular profiles,xT DNA sequencing and whole-transcriptome analysis were conducted onmatched tumor-TO pairs. TOs were evaluated with available archivalsource tumor tissue for matched xT DNA sequencing and wholetranscriptome analysis (n=32 with xT DNA; n=18 with RNA-Seq). Toinvestigate the degree to which TOs and their source tumors exhibitedsimilar genomic profiles, frequencies of somatic alterations classifiedas pathogenic, likely pathogenic, or variant of unknown significance(VUS) were first examined and confirmed consistent co-occurrence andmutual exclusivity patterns across groups (FIG. 20A). The rates ofsomatic variant recapitulation and CNV concordance (FIG. 20B) were thenevaluated. On average, somatic recapitulation of source tumor variants(variant allele fraction [VAF]>10%) in the TOs had rates >76.9% (withincancer ranges 70%-92%) and CNV concordance was >77.6% (within cancerranges 69%-86%). Three outlier organoid samples with low somaticrecapitulation rates were identified: two derived from NSCLC and onefrom ovarian carcinoma. The ovarian source tumor was driven by aloss-of-function BRCA1 germline mutation plus loss of heterozygosity(LOH), both of which were recapitulated in the corresponding organoid.The remaining variants reflected passenger events from resultant genomicinstability. For the two NSCLC outliers, one TO failed to recapitulatethe source tumor's KRAS Q61H driver mutation and the other TO, itssource tumor's STK11 mutation, suggesting outgrowth of eitheralternative clones from tumor heterogeneity or dysplastic epitheliumfrom field effect. After adjusting for purity in the source tumor, VAFanalysis of recapitulated pathogenic or likely pathogenic variantsprovided further support for overall genetic concordance (R²=0.22) butalso showed a set of TOs with somatic variants that expanded to highfrequencies (FIG. 20C). Overall, these genetic concordance patternsindicate several events consistent with recapitulation of tumorheterogeneity and others demonstrating clonal selection.

Overall transcriptomic concordance was next assessed between TOs andtheir corresponding source tumors. Higher mean rank correlation betweenTOs and source tumor pairs (R=0.845 [0.690-0.935]) were observed whencompared to randomly sampled sets of tumor and organoid pairs(P=3.4×10⁻⁹; R=0.797 [0.664-0.90]; FIG. 20D).

d. Human Leukocyte Antigen Loss of Heterozygosity in Tumor Organoids

It was next sought to functionally validate recapitulation of apan-cancer genetic alteration between TOs and source tumors. Loss ofheterozygosity of the Human Leukocyte Antigen Class I (HLA LOH) wasrecently shown to be an important biomarker for immunotherapy responsewith major implications for the development of targeted immunotherapies(McGranahan et al., 2017; Chowell et al., 2016) across cancers.Therefore, the preservation of HLA LOH in TOs were investigated. Pairednormal sequencing from normal tissue or plasma buffy coat for 22 sampleswere leveraged, and 5 (22.7%) tumor cases were identified where one ofthe HLA alleles demonstrated a comparative reduction in DNA coverageindicating LOH. For an example of identifying HLA LOH, see U.S. patentapplication Ser. No. 16/789,413, titled “Detection Of Human LeukocyteAntigen Class I Loss Of Heterozygosity In Solid Tumor Types By NGS DNASequencing” and filed Feb. 12, 2020, the contents of which areincorporated by reference herein in their entirety and for all purposes.One respective paired TO was selected for functional validation. DNAsequencing demonstrated a complete loss of the A*02:01 allele in thisTO, purifying the heterogeneous signal in the parent tumor (FIG. 20E,F).This was confirmed with flow cytometry, which demonstrated the TOretained surface expression of the HLA allele that was predicted to beretained (A*03:01), and absence of the allele predicted to be lost(A*02:01) (FIG. 20F).

e. Tumor Organoids Maintain Oncogenic Signatures Representative of EachCancer Type

Having confirmed TO recapitulation of source tumors in this smallcohort, a cohort level pan-cancer molecular characterization of TOs wascarried out and a comparison was made to an independent cohort ofrepresentative cancer patients. An additional 200 sequentiallyestablished TO cultures for Tempus xT testing was targeted, includingwhole transcriptome analysis, yielding a total of 230 TOs with DNA and177 with RNA data. TO's genomic and transcriptomic profiles were thencompared to an independent cohort of 261 patient tumors with relevantcancer types previously profiled with the same assay as part of thexT500 cohort (Beaubier et al., 2019a) and also pooled the 32 patienttumors used in the tumor:TO concordance analysis. A gene-based test wasemployed to compare the frequency of mutations between TOs and tumorswithin each cancer type, using variants classified as pathogenic, likelypathogenic, or of unknown significance (data not shown). Statisticallysignificant differences were not detected from any genes, indicating theTO cohort is largely comparable to other large-scale genomic sequencingcohorts of clinical specimens (Zehir et al., 2017).

Unsupervised RNA clustering of the 50 most variable MSigDB C₆ oncogenicsignatures in TOs revealed clustering by cancer type, indicating invitro maintenance of the disrupted transcriptional pathways that areassociated with each cancer type (data not shown). For example, colonadenocarcinoma TOs clustered apart from other cancer types, withupregulated cell cycle- and Wnt-related pathways clustering together.Meanwhile, breast cancer organoids show clustering of upregulated HER2and NOTCH pathways (data not shown).

f. Differentially Activated Pathways Between Tumors and Tumor Organoids

To characterize the biological differences between TOs and tumors, anevaluation of the differences in pathway disruption between all patienttumors and available TOs in cancer types with at least 10 unmatched Toswas performed. Single-sample GSEA (ssGSEA) scores were calculated forthe 50 mSigDB Hallmark pathways (Liberzon et al., 2015) and tested fordifferences in pathway scores using a linear model with covariatesapplied to account for the 18 matched TO and source tumor pairs whenapplicable. As anticipated, expression variability was dominated bygrowth and immune pathways indicative of stromal elimination in TOs(data not shown). To control for this, a second analysis was performed,including tumor purity as a covariate. A substantial proportion ofpathway activity was preserved across cancer types, with nearly half(24/50) of Hallmark pathways showing significant differences in two orfewer of the six considered cancer types. Several pathways that showedincreased activation were growth- or metabolism-related, including PI3K,estrogen signaling, glycolysis, oxidative phosphorylation, xenobioticmetabolism, and fatty acid metabolism (FIG. 21C). It was presumed thatthese changes reflect intrinsic characteristics of in vitro culture(e.g., sustained exposure to growth factors). Notable exceptions to thispattern are colon and pancreatic cancer, which both exhibit manydisparate pathways with decreased activation, including immune andgrowth pathways. The mechanisms underlying the bias towards decreasedactivation in these two cancers, as well as their notable similarity,are unclear and require further validation and study, although thegenetic complexity of these cancer types may be a factor.

Mutation-driven oncogenic signaling pathway disruption is established asa primary contributor of oncogenesis and tumor maintenance (Sanchez-Vegaet al., 2018). Therefore, to determine whether RNA-based pathwayactivation scores in mutated pathways were maintained within theorganoids, the correspondence between mutation signatures and relevantssGSEA Hallmark pathway disruption was tested for classical growth andapoptosis evasion pathways. As expected, Wnt disruption via pathogenicmutations in APC led to significantly higher Wnt pathway activity in apan-cancer model (P=1.7×10⁻⁵), whereas P53 disruption significantlyabrogated P53 signaling (P=1.5×10⁻¹¹; FIG. 21D). A significantdifference was also observed between KRAS mutant and KRAS WT samples forthe upregulated KRAS signaling signature among colon cancer TOs(P=1.7×10⁻⁴) but not pan-cancer likely due to tonic EGFR activation fromreceptor ligands present in media, i.e. EGF (data not shown). Inaddition, the estrogen transcriptional response was compared acrossbreast, endometrial, and ovarian cancer TOs, with breast cancer TOsconsistently showing the highest levels compared to all other TOs inearly response genes (P=1.9×10⁻⁶; FIG. 21E). These results show thatcommonly mutated genes drive transcriptional changes in oncogenicsignaling pathways in TOs in a manner consistent with that observed insource tumors.

g. Growth Factor Requirements for Organoid Culture

Previous reports suggest mutational profiles of TOs govern niche growthfactor requirements in cancers (Fujii et al., 2016; Sato et al., 2011).Since transcriptional changes in oncogenic signaling pathways in TOswere observed, the extent to which these changes resulted in functionalphenotypes that support TO growth in cancer subtypes was investigated.First, the quantified growth in colon cancer TOs (n=10) was quantifiedusing combinations of peptide and protein growth factors in a mannersimilar to Fujii et al. (2016) (FIG. 22A). TO growth was measured usingautomated high-throughput brightfield imaging (FIG. 22B). Consistentwith Fujii et al. 2016, colon cancer organoids propagated in the absenceof Wnt-3A and R-spondin 1 (RSPO-1), attributable to APC inactivation(FIG. 22C).

These experiments were then extended to an additional 40 TOsrepresenting other prevalent histologies in the biobank (FIG. 22D).While epidermal growth factor (EGF) stimulated proliferation in allcultures, most TO lines exhibited no significant differences in growthbetween various media compositions (ANOVA P<0.05). However, in breastand ovarian cancer TO lines, fibroblast growth factors (FGFs) enhancedpropagation in some cases (FIG. 22D), consistent with previous reportsHill et al., 2018; Sachs et al., 2018). Wnt-3A and RSPO-1 dependence inpancreatic cancer TO lines (FIG. 22E) was confirmed and also consistentwith previous reports (Boj et al., 2015; Seino et al., 2018; Tiriac etal., 2018). Interestingly, some TOs did not appear to require anymitogen growth factors (FIG. 22D). Evaluation of the mutations in theseTOs revealed a high degree of aneuploidy as compared to TOs that wereEGF-dependent (data not shown).

It was then investigated how gene expression is affected by mediaconditions. Four pancreatic cancer lines were selected and one of eachfrom breast, colon, head and neck (HNSCC), and lung cancer TO lines thatwere cultured in EGF-containing media with and without additional growthfactors for RNA-Seq. PCA analysis demonstrated that the primarydeterminant of transcriptomic differences was cancer type (FIG. 22F).GSEA of this limited data set revealed upregulation of housekeeping andcell cycle genes within media containing Wnt-3A and RSPO-1 as comparedto other media types, which is consistent with the phenotypicobservations in pancreatic TOs (FIG. 22E). While these experiments didnot illustrate a significant difference between growth factorrequirements in the majority of cases, these findings could be due toTOs adapting to in vitro culture conditions and minimizing thedependence on specific niche factors. Next, organoid establishment uponinitiation of TO cultures from 100 sequential non-pancreatic tumorsgrown in either minimal or complete media was evaluated. No significantdifferences between minimal (type B) and complete media (type F) in theproliferation and formation of TOs from primary tumor single cellsuspensions were observed. (FIG. 22G).

h. Development of a Label-Free Universal Organoid Drug Screening Assay

It was next sought to develop a robust and reliable assay to profiletherapeutics in vitro. Given the evidence of intratumoral clonalheterogeneity within the cultures (FIG. 20C), an assay was sought thatcould measure therapeutic response heterogeneity. It was reasoned thatsince organoids form via clonal outgrowths of single cells, withmultiple organoids growing within each well, measuring drug response atsingle organoid resolution could be achieved via microscopy.Additionally, measuring response for each TO would increase the numberof technical replicates to hundreds per well, providing highly accuratemeasurements of mean drug response as compared to previous reports thatmeasure responses at the micro-well level using biochemical assays (Ooftet al., 2019; Tiriac et al., 2018; van de Wetering et al., 2015). It wasalso desired to ensure high throughput, low-cost, and scalability.Recent advances in computational prediction of fluorescent signals fromunlabeled light microscopy (Christiansen et al., 2018; Ounkomol et al.,2018), as well as conditional generative adversarial networks (GANs),such as Pix2Pix (Isola et al., 2017) and CycleGAN (Zhu et al., 2017),have enabled realistic translation and prediction of images from onedomain (i.e., black and white photographs) to another domain (i.e.,color photographs). However, the application of GAN-based style-transferto patient-derived TOs has not been described. A light microscopy-basedassay was developed to eliminate the use of costly vital dyes, enablerapid image acquisition, and minimize phototoxicity in time-lapseimaging.

Since a robust ground truth readout of TO viability required, ahigh-content image analysis was first performed with standard invertedmicroscopy to measure viability and apoptosis (STAR methods, FIG.23A,B). Binary classification of vital dye labeling of individual cellsthus enabled us to acquire comprehensive measurements of TO viability,apoptosis and proliferation (by number of viable cells per TO) (FIG.23A,B).

The drug screening assay was optimized for 384-well plates, with Z primevalues ranging from 0.882 to 0.975, which is suitable for screeningassays (Narasimhan et al., 2020; Zhang et al., 1999). The screeningassay's reliability and reproducibility was benchmarked on two distinctTO lines that represented solid (gastric cancer) and cystic (coloncancer, CRC) organoid morphology. Two independent rounds of screeningwere performed with a library of 351 chemotherapeutics and smallmolecule inhibitors. To measure compound potency, the inverse area underthe curve (AUC) of dose-response curves was calculated from normalizedviability measurements. There was high correlation between the tworounds of experiments for proliferation, apoptosis, and viability, andall readouts outperformed metabolic assays performed in parallel (MTSassays).

Next, the assay's suitability for high-throughput screening wasdetermined. The sensitivity and specificity in a dose-response serieswas evaluated, with dosing based on TO viability at 10 μM. Using aninverse AUC cutoff of 5 to classify effective drugs, it was determinedthat the microscopy-based measurements outperformed MTS assays viareceiver operating curve (ROC) analysis for both TO lines tested (ROCAUC values: gastric=0.906 cell death, 0.917 apoptosis, compared to MTS0.801; CRC=0.923 cell death, 0.929 apoptosis, compared to MTS 0.843).Unsupervised hierarchical clustering of drug response was performed andit was found that measures of cell death and viability largelyoverlapped, but there were drugs that clustered by viability readout,representing agents with cytostatic, not cytotoxic effects (FIG. 23D).Extending these analyses across two TO lines showed that drug responsesclustered by TO line and not by assay readout, confirming biologicspecificity (data not shown). Finally, given that drug responses weremeasured at single TO resolution, individual TO response distributionswere examined. Evidence of heterogeneity of TO drug response was foundin some TOs, whereas others exhibited a more uniform drug response (datanot shown).

i. Neural Network-Based Model for Predicting TO Drug Response

After the establishment of a ground-truth readout, a neuralnetwork-based model was developed to generate data from fluorescentreadouts from light microscopy images alone. Moreover, owing to theflexibility of neural network architectures, recent work where GANs havebeen adapted to regression tasks as a framework for response predictionwas drawn upon (Aggarwal et al., 2020; Olmschenk et al., 2019). It wasreasoned that a single framework could enable both the prediction offluorescent stain and response directly from brightfield images (FIG.24A). To that end, a preliminary high-throughput drug screening modelwas developed and referred to as a Regularized Conditional Adversarial(RCA) network, which is an extension of Pix2pix (Isola et al. 2017), andincludes an additional network to predict overall viability perbrightfield image (FIG. 24A).

The RCA network was trained on 8,415 paired brightfield and 3-channelfluorescence images from the colon adenocarcinoma TO screeningexperiments (FIG. 23), each with associated calculated drug responsesbased on TO-PRO-3 viability. The RCA model combines the brightfield andgenerated fluorescence readout to predict an overall average viabilityusing the Viability Discriminator Network (FIG. 24A, see STAR methodsfor details on training). The RCA network was benchmarked against the351-compound screen from the colon adenocarcinoma TO and found that itdemonstrated highly significant correlations between ground truth andpredicted organoid response in this TO with cystic morphology (FIG.24C). Extending this analysis to a gastric cancer TO line with a solidTO morphology again resulted in excellent correlation (Pearsoncorrelation=0.91, FIG. 24C). Representative images of real versusgenerated fluorescence demonstrated nearly indistinguishable visualmatching (FIG. 24B). These results were confirmed using two quantitativemetrics: the structural similarity index (SSIM) as well as the root meansquared error (RMSE). The reported average SSIM and RMSE values across1,526 samples of the colon adenocarcinoma TO used in the screeningexperiment were 0.90 and 0.13924 respectively. For the gastric TO line,the reported average SSIM and RMSE values across 9200 samples were 0.898and 0.136, respectively.

Next, the ability of the RCA network was investigated to discriminateclinically relevant drug responses. Differentially active compoundsbetween the two TO lines as determined by the RCA network was firstevaluated. In the colon TO line, sensitivity to drugs that inhibit MEK,a critical component of the mitogen-activated protein kinase (MAPK)pathway (FIG. 24D), was observed. The dose-response curve fortrametinib, a potent MEK1 and MEK2 inhibitor, showed a similar responsefor TO viability measured by TO-PRO-3, Caspase-3/7, and predicted TOviability from the RCA (FIG. 24D). Analysis of the molecular profile ofthe colon TO revealed a KRAS G12V missense mutation as well as acopy-number amplification of BRAF, both of which are commonly associatedwith CRC and upregulate the MAPK pathway (Vaughn et al. 2011). Incontrast, the gastric cancer TO line was enriched for compounds withpurported activity against EGFR and ERBB2 (HER2) receptor tyrosinekinases (e.g. Afatinib) (FIG. 24E). Analyzing the genomic profile ofthis TO revealed a significant amplification of ERBB2 (copy number>20),suggesting the potency of these agents was due to a dependence of ERBB2copy gain (FIG. 24F).

To further test the RCA network's generalizability, a precision medicinescenario generalizable across multiple cancer indications was sought. Arecent development in precision oncology is the use of poly (ADP-ribose)polymerase (PARP) inhibitors in patients whose tumors exhibit evidenceof homologous recombination deficiency (HRD), especially in cases ofsomatic loss of BRCA1 or BRCA2, or LOH of functional BRCA1/2 alleles inpatients with inherited non-functional BRCA1/2 alleles. Ten additionalTO lines (breast, endometrial, colon, ovarian, and NSCLC) were thenevaluated for genome-wide LOH indicative of HRD, and categorized TOs aseither HRD-positive or HRD-negative (Timms et al., 2014; Yi et al.,2019). TOs were then exposed to a panel of FDA-approved PARP inhibitorsand compared RCA network-predicted drug responses to fluorescent-basedreadouts. It was determined that the RCA network predictions werestrongly correlated with fluorescent-based drug responses across allcancer types (data not shown). Additionally, the RCA network'smeasurement of PARPi response was able to discriminate betweenHRD-positive and -negative organoids comparable to fluorescent-basedmeasurements (FIG. 24G). Taken together, the RCA network-based assayaccurately predicts clinically relevant drug responses in a universallabel-free manner.

4. Discussion

A pan-cancer patient-derived TO platform is presented for precisionmedicine applications. The unprecedented scale of this study elucidatedkey insights in TO production, molecular characterization, and in vitroprofiling that carry implications for the future of TO use in researchand clinical settings. For example, the results from more than 1,000cultures across multiple tumor types confirm that TO growth is unbiasedin terms of clinical phenotypes. Moreover, best practices in TOdevelopment were informed, such as obtaining sufficiently sized biopsyor resection specimens in order to yield adequate cellular biomass forhigh-proliferation cultures. Crucially, the molecular landscape of thispan-cancer TO cohort represents those seen in previously sequencedpatient cohort (Beaubier et al., 2019a) and mirrored previous TO cohortsof individual cancer types (Driehuis et al., 2019; Fujii et al., 2016;Hill et al., 2018; Sachs et al., 2018). It was also confirmed that TOsrecapitulate structural genomic features of the tumor including thelocus of HLA class I alleles. As HLA loss is an immune evasion mechanism(McGranahan et al., 2017), these findings suggest that HLA genotype andLOH should be considered when utilizing TOs in immuno-oncology researchand drug discovery.

Transcriptomic analyses of TOs confirmed conservation of relevant tumorsignaling pathways and led to the investigation of which growth factorswere sufficient to initiate and propagate organoids. In contrast toseveral seminal studies on TO culture (Boj et al., 2015; Dijkstra etal., 2020; Sachs et al., 2018; Seino et al., 2018), it was found thatEGF and Noggin were sufficient for the majority of organoids with theexception of certain gastric and pancreatic cancers as previouslydescribed (Fujii et al., 2016; Seino et al., 2018). Furthermore, drugscreens under minimal media conditions uncovered clinically relevanttherapeutic sensitivities. These findings suggest future organoidstudies and biobanking efforts such as the Human Cancer ModelsInitiative (Gerhard et al., 2020) can be achieved with less complexmedia formulations, thus amounting to substantial reductions in time,cost, user error, and methodological variation. Comprehensive pan-cancerstudies should be conducted to determine genotypes that require morebespoke media formulations, as accomplished in pancreatic cancer (Seinoet al., 2018).

To advance TO utility for drug development and precision medicine, auniversal label-free TO drug screening assay was also devised. Withoutsacrificing throughput, the RCA network prediction of drug response fromlight microscopy achieved high reproducibility compared to previouslydescribed metabolic-based assays (Tiriac et al., 2018; Tiriac et al.,2020; Vlachogiannis et al., 2018). Importantly, the RCA networkidentified clinically relevant drug responses across a broad range ofcancer-types including PARPi response from HRD-positive and -negativeTOs. Notably, the PARPi responses in this small cohort of organoids weregenerally higher in HRD-positive TOs, with certain drugs exhibiting morepotency in vitro than others. These observations confirm previousstudies in patients (Coleman et al., 2017) and short-term ovarian cancerorganoid cultures (Hill et al., 2018). In addition, the RCA networkidentified response to afatinib associated with a high ERBB2 (HER2)amplification in the gastric TO. The distribution of response among theTO populations was demonstrative of functional intratumoralheterogeneity, as evidenced by a subset of the organoid populationexhibiting resistance to afatinib. When correlated with the patient'sclinical course, it was found that the biopsy used to establish this TOline was obtained during disease progression on anti-ERBB2 therapy.Identifying heterogeneity in drug response has the potential to uncovermechanisms of primary resistance to novel therapeutics and may alsoprovide predictive and prognostic information for treatment response inpersonalized clinical assays.

Deep learning light microscopy-based assays are not only capable ofdetermining drug sensitivities but may do so with far less biomassinput, especially if longitudinal time-lapse imaging is used. As opposedto endpoint measurements, time-lapse reduces the number of conditionsand replicates required to measure drug efficacy potentially overcomingsignificant barriers in personalized in vitro drug testing by decreasingthe interval to expand TOs between biopsy and drug assays. In fact, arecent endpoint-based in vitro organoid drug assay performed in aCLIA-certified laboratory reported an average 8-week interval betweeninitiation of organoid culture and drug profiling (Narasimhan et al.,2020). Based on these findings, it is believed that lightmicroscopy-based deep learning holds the potential to provide actionableresults in more clinically applicable timeframes.

The comprehensive data and robust methods described in this study willhopefully serve to advance the application of organoids in precisiononcology research, especially systems biology approaches centered onconnecting molecular features of these models to therapeutic response.In summary, it is believed that this pan-cancer organoid platform willbe a valuable resource to cancer researchers especially those pursuingprecision oncology.

L. Example 12—High Throughput Tumor Organoid Drug Screen

In this example, data for each therapy is presented as a histogram. Foreach range of viability percentages (for example, ˜0-5%, ˜5-10%,˜10-15%, . . . ˜95-100%, etc.) on the γ-axis, the histogram shows thenumber of organoids associated with that viability percentage range(along the x-axis). Viability was determined by the systems and methodsdisclosed above.

Each tumor organoid line in these examples was cultured individually inwells of a tissue culture plate, as described above.

Each well of tumor organoids was exposed to one of the followingtherapies: A-196 (inhibitor of SUV4-20 or SUV420H1 and SUV420H2),Afatinib (tyrosine kinase inhibitor), Adapalene (retinoid-likecompound), Adavosertib (MK-1775, wee1 kinase inhibitor), Alectinib(CH5424802, anaplastic lymphoma kinase inhibitor), Alisertib (MLN8237,aurora A kinase inhibitor), Aphidicolin (reversible inhibitor ofeukaryotic nuclear DNA replication, antimitotic), Azacitidine (anantimetabolite antineoplastic agent, a chemotherapy), AZ20 (ataxiatelangiectasia and Rad3-related protein/ATR kinase inhibitor), AZ31(ataxia-telangiectasia mutated/ATM kinase inhibitor), AZD6738 (ataxiatelangiectasia and Rad3-related protein/ATR kinase inhibitor), AZD7762(checkpoint kinase inhibitor), Barasertib (AZD1152-HQPA, aurora B kinaseinhibitor), BAY-1895344 (ATR and ATM kinase inhibitor), Berzosertib (ATRand ATM kinase inhibitor), BIO-acetoxime (GSK-3a/b inhibitor),Bortezomib (proteasome inhibitor), Cabozantinib (kinase inhibitor,inhibitor of AXL, RET, and tyrosine kinases c-Met and VEGFR2),Capecitabine (an antimetabolite antineoplastic agent, a chemotherapy),Carboplatin (an alkylating antineoplastic agent, a chemotherapy), CC-115(DNA-PK and mTOR inhibitor), CC-223 (inhibitor of mammalian target ofrapamycin/mTOR), CCT-245737 (checkpoint kinase 1/CHK1 inhibitor),CD-2665 (retinoic acid receptor β (RARβ)/RARγ antagonist), CD-437(retinoic acid receptor (RAR)γ-selective agonist, γ-selective retinoid;inducer of apoptosis), CDK2 inhibitor II, CH-55 (RAR agonist), Cisplatin(an alkylating antineoplastic agent, a chemotherapy), Cladribine (anantimetabolite antineoplastic agent, a chemotherapy), Cytarabine (anantimetabolite antineoplastic agent, a chemotherapy), Dasatinib (atyrosine kinase inhibitor antineoplastic agent, a chemotherapy),Docetaxel (an antimicrotubular antineoplastic agent, a chemotherapy),Doxorubicin (Adriamycin, a topoisomerase inhibitor antineoplastic agent,a chemotherapy), Empagliflozin (BI 10773, a sodium-glucosecotransporter-2/SGLT2 inhibitor), Entrectinib (RXDX-101, tyrosine kinaseinhibitor, inhibitor of the tropomyosin receptor kinases A, B and C,C-ros oncogene 1 and anaplastic lymphoma kinase), Epirubicin (atopoisomerase inhibitor antineoplastic agent, a chemotherapy), Etoposide(a topoisomerase inhibitor antineoplastic agent, a chemotherapy),Everolimus (inhibitor of mTOR), Fluorouracil/5-FU (an antimetaboliteantineoplastic agent, a chemotherapy), GDC-0349 (inhibitor of mTOR),GDC-0575 (ARRY-575, CHK1 inhibitor), Gemcitabine (an antimetaboliteantineoplastic agent, a chemotherapy), GSK2292767 (inhibitor ofphosphatidylinositol 3-kinase/PI3K), GSK-872 (GSK2399872A, kinaseinhibitor, inhibitor of RIP3K), Hesperadin (aurora kinase inhibitor),Hydroxyurea (an antimetabolite antineoplastic agent, a chemotherapy),Ifosfamide (an analog of cyclophosphamide, an alkylating antineoplasticagent, a chemotherapy), Ipragliflozin (ASP1941, an SGLT2 inhibitor),KYA1797K (Wnt/β-catenin inhibitor), Lapatinib (tyrosine kinase inhibitorthat interrupts the HER2/neu and epidermal growth factor receptor/EGFRpathways, an antineoplastic agent, a chemotherapy), Larotrectinib(inhibitor of tropomyosin kinase receptors TrkA, TrkB, and TrkC), LDC4297 (Cyclin-dependent kinase/CDK inhibitor, CDK7 inhibitor), Lenvatinib(multiple kinase inhibitor, inhibitor of VEGFR1, VEGFR2 and VEGFR3kinases), LY3023414 (DNA-PK/PI3K/mTOR Inhibitor), Methotrexate (anantimetabolite antineoplastic agent, a chemotherapy), Nelarabine (anantimetabolite antineoplastic agent, a chemotherapy), Niraparib(MK-4827, a poly ADP ribose polymerase/PARP inhibitor), NSC 23766(inhibitor of Rac GTPase), Olaparib (PARP inhibitor), Oxaliplatin (analkylating antineoplastic agent, a chemotherapy), Paclitaxel (a taxane,an antimicrotubular antineoplastic agent, a chemotherapy), Pamiparib(BGB-290, PARP inhibitor), PFI-4 (Bromodomain And PHD Finger Containing1/BRPF1 bromodomain inhibitor), PHA-767491 HCl (Mitogen-activatedprotein kinase-activated protein kinase 2/MK2 and CDK inhibitor),PLX7904 (RAF inhibitor), Pracinostat (histone deacetylase/HDACinhibitor), Pralatrexate (an antimetabolite antineoplastic agent, achemotherapy), Prexasertib HCl (checkpoint kinase 1/CHK1 inhibitor),RO-3306 (CDK1 inhibitor), Rucaparib (PARP inhibitor), Selpercatinib(LOXO-292, ARRY-192, a tyrosine kinase inhibitor), SIS3 HCl(TGF-beta/Smad inhibitor), SMI-4a (Pim kinase inhibitor), SN-38(inhibitor of DNA topoisomerase I, active metabolite ofCPT-11/Irinotecan), ST-1926 (Adarotene, atypical retinoid, apoptosisinducer), Staurosporine (multi-kinase inhibitor used as a positivecontrol), Talazoparib (BMN-673, PARP inhibitor), TCS 359 (fms-liketyrosine kinase-3/FLT3 inhibitor), Tenalisib (RP6530, a PI3K δ/γinhibitor), Tozasertib (VX-680, MK-0457, an Aurora Kinase inhibitor),Trametinib (GSK1120212, a MEK inhibitor), Ulixertinib (inhibitor ofextracellular signal-regulated kinase/ERK 1 and 2, with potentialantineoplastic activity), Veliparib (ABT-888, PARP inhibitor),Vinblastine (an antimicrotubular antineoplastic agent, a chemotherapy),or VX-984 (DNA-dependent protein kinase/DNA-PK inhibitor).

In another example, a well of tumor organoids may be exposed to one ofthe following therapies or combination therapies: afatinib plus METinhibitor (for example, tivantinib, cabozantinib, crizotinib, etc.),AZ31 plus SN-38, bevacizumab (anti-VEGF monoclonal IgG1 antibody),cetuximab (epidermal growth factor receptor/EGFR inhibitor), crizotinib(a tyrosine kinase inhibitor antineoplastic agent), cyclophosphamide (analkylating antineoplastic agent), erlotinib (epidermal growth factorreceptor inhibitor antineoplastic agent), FOLFIRI, bevacizumab plusFOLFIRI, FOLFOX, gefitinib (EGFR inhibitor), gemcitabine plus docetaxel,pemtrexed (an antimetabolite antineoplastic agent), ramucirumab(Vascular Endothelial Growth Factor Receptor 2/VEGFR2 Inhibitor), ortopotecan (a topoisomerase inhibitor).

1. Gastric Cancer

In this example, tissue culture wells containing a gastric cancer tumororganoid line were each exposed to one of the therapies listed above. Inthis example, an ERBB2 variant (mutation) was detected when the tumororganoids were genetically sequenced.

FIG. 25A is a histogram summarizing the caspase 3/7 readout results forGSK inhibitor BIO-axetocime (10,000 nM dose). Approximately 150organoids had 0% viability and were susceptible to the drug.Approximately 20 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 15%. This histogram indicates thegeneral absence of organoids associated with this gastric cancerorganoid line that are resistant to BIO-acetoxime.

FIG. 25B is a histogram summarizing the caspase 3/7 readout results forproteasome inhibitor Bortezomib (10,000 nM dose). Approximately 225organoids had 0% viability and were susceptible to the drug.Approximately 15 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 15%. This histogram indicates thegeneral absence of organoids associated with this gastric cancerorganoid line that are resistant to Bortezomib.

FIG. 25C is a histogram summarizing the caspase 3/7 readout results forHDAC inhibitor pracinostat (10,000 nM dose). Approximately 120 organoidshad 0% viability and were susceptible to the drug. Approximately 10organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 20%. This histogram indicates the general absenceof organoids associated with this gastric cancer organoid line that areresistant to pracinostat.

2. Lung Cancer 1

In this example, tissue culture wells containing a first lung cancertumor organoid line were each exposed to one of the therapies listedabove.

FIG. 26A is a histogram summarizing the caspase 3/7 readout results forantimetabolite chemotherapy gemcitabine (10,000 nM dose). Approximately50 organoids had 0% viability and were susceptible to the drug.Approximately 15 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 35%. This histogram indicates thepresence of a small proportion of organoids associated with this lungcancer organoid line that are resistant to gemcitabine.

FIG. 26B is a histogram summarizing the caspase 3/7 readout results fortopoisomerase inhibitor SN-38 (10,000 nM dose). Approximately 20organoids had 0% viability and were susceptible to the drug.Approximately 20 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 45%. This histogram indicates thepresence of organoids associated with this lung cancer organoid linethat are resistant to SN-38.

FIG. 26C is a histogram summarizing the caspase 3/7 readout results forantimetabolite chemotherapy gemcitabine (10,000 nM dose). Approximately23 organoids had 0% viability and were susceptible to the drug.Approximately 15 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 40%. This histogram indicates thepresence of organoids associated with this lung cancer organoid linethat are resistant to aphidicolin.

FIG. 26D is a histogram summarizing the caspase 3/7 readout results forPARP inhibitor Niraparib (10,000 nM dose). Approximately 80 organoidshad 0% viability and were susceptible to the drug. Approximately 20organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 25%. This histogram indicates the presence of asmall proportion of organoids associated with this lung cancer organoidline that are resistant to Niraparib.

3. Lung Cancer 2

In this example, tissue culture wells containing a second lung cancertumor organoid line were each exposed to one of the therapies listedabove.

FIG. 27A is a histogram summarizing the caspase 3/7 readout results forantimicrotubular chemotherapy docetaxel (10,000 nM dose). Approximately50 organoids had 0% viability and were susceptible to the drug.Approximately 20 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 30%. This histogram indicates thepresence of a small proportion of organoids associated with this lungcancer organoid line that are resistant to docetaxel.

FIG. 27B is a histogram summarizing the caspase 3/7 readout results forantimitotic chemotherapy aphidicolin (10,000 nM dose). Approximately 50organoids had 0% viability and were susceptible to the drug.Approximately 15 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 30%. This histogram indicates thepresence of a small proportion of organoids associated with this lungcancer organoid line that are resistant to aphidicolin, which is asmaller resistant proportion than was detected in the first lung tumororganoid line.

FIG. 27C is a histogram summarizing the caspase 3/7 readout results fortopoisomerase inhibitor SN-38 (10,000 nM dose). Approximately 100organoids had 0% viability and were susceptible to the drug.Approximately 5 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 20%. This histogram indicates thegeneral absence of organoids associated with this lung cancer organoidline that are resistant to SN-38, unlike the resistant proportion seenin the first lung tumor organoid line.

FIG. 27D is a histogram summarizing the caspase 3/7 readout results forantimetabolite chemotherapy gemcitabine (10,000 nM dose). Approximately30 organoids had 0% viability and were susceptible to the drug.Approximately 10 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 35%. This histogram indicates thepresence of a small proportion of organoids associated with this lungcancer organoid line that are resistant to gemcitabine.

4. Head and Neck Cancer

In this example, tissue culture wells containing a head and neck cancertumor organoid line were each exposed to one of the therapies listedabove.

FIG. 28A is a histogram summarizing the caspase 3/7 readout results forantimicrotubular chemotherapy paclitaxel (10,000 nM dose). Approximately65 organoids had 0% viability and were susceptible to the drug.Approximately 35 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 40%. This histogram indicates thepresence of a small proportion of organoids associated with this headand neck cancer organoid line that are resistant to paclitaxel.

FIG. 28B is a histogram summarizing the caspase 3/7 readout results forkinase inhibitor LY3023414 (10,000 nM dose). Approximately 80 organoidshad 0% viability and were susceptible to the drug. Approximately 5organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 15%. This histogram indicates the general absenceof organoids associated with this head and neck cancer organoid linethat are resistant to LY3023414.

5. Endometrial Cancer 1

In this example, tissue culture wells containing a first endometrialcancer tumor organoid line were each exposed to one of the therapieslisted above.

FIG. 29A is a histogram summarizing the caspase 3/7 readout results foralkylating chemotherapy cisplatin (10,000 nM dose). Approximately 60organoids had 0% viability and were susceptible to the drug.Approximately 5 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 20%. This histogram indicates thegeneral absence of organoids associated with this endometrial cancerorganoid line that are resistant to cisplatin.

FIG. 29B is a histogram summarizing the caspase 3/7 readout results foralkylating chemotherapy oxaliplatin (10,000 nM dose). Approximately 20organoids had 0% viability and were susceptible to the drug.Approximately 18 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 40%. This histogram indicates thepresence of organoids associated with this endometrial cancer organoidline that are resistant to oxaliplatin, unlike the cisplatin. In lightof these results, a report may indicate that cisplatin is ranked higherthan oxaliplatin as a therapy that is likely to be effective.

FIG. 29C is a histogram summarizing the caspase 3/7 readout results forapoptosis inducer ST-1926 (10,000 nM dose). Approximately 45 organoidshad 0% viability and were susceptible to the drug. Approximately 10organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 30%. This histogram indicates the general absenceof organoids associated with this endometrial cancer organoid line thatare resistant to ST-1926.

6. Endometrial Cancer 2

In this example, tissue culture wells containing a second endometrialcancer tumor organoid line were each exposed to one of the therapieslisted above.

FIG. 30A is a histogram summarizing the caspase 3/7 readout results forapoptosis inducer ST-1926 (10,000 nM dose). Approximately 65 organoidshad 0% viability and were susceptible to the drug. Approximately 40organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 40%. This histogram indicates the presence of asmall proportion of organoids associated with this endometrial cancerorganoid line that are resistant to ST-1926, and may indicate that thisorganoid line is more resistant to ST1926 than the endometrial cancerorganoid line in the example above.

FIG. 30B is a histogram summarizing the caspase 3/7 readout results forkinase inhibitor Prexasertib (10,000 nM dose). Approximately 80organoids had 0% viability and were susceptible to the drug.Approximately 35 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 40%. This histogram indicates thepresence of a small proportion of organoids associated with thisendometrial cancer organoid line that are resistant to Prexasertib.

FIG. 30C is a histogram summarizing the caspase 3/7 readout results forkinase inhibitor hesperadin (10,000 nM dose). Approximately 100organoids had 0% viability and were susceptible to the drug.Approximately 5 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 20%. This histogram indicates thegeneral absence of organoids associated with this endometrial cancerorganoid line that are resistant to hesperadin.

7. Colon Cancer

In this example, tissue culture wells containing a colon cancer tumororganoid line were each exposed to one of the therapies listed above.

FIG. 31A is a histogram summarizing the caspase 3/7 readout results forantimetabolite chemotherapy Fluorouracil (10,000 nM dose). Approximately70 organoids had 0% viability and were susceptible to the drug.Approximately 10 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 25%. This histogram indicates thegeneral absence of organoids associated with this colon cancer organoidline that are resistant to Fluorouracil.

FIG. 31B is a histogram summarizing the caspase 3/7 readout results forkinase inhibitor VX-984 (10,000 nM dose). Approximately 35 organoids had0% viability and were susceptible to the drug. Approximately 25organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 40%. This histogram indicates the presence oforganoids associated with this colon cancer organoid line that areresistant to VX-984.

FIG. 31C is a histogram summarizing the caspase 3/7 readout results forantimicrotubular chemotherapy Docetaxel (10,000 nM dose). Approximately70 organoids had 0% viability and were susceptible to the drug.Approximately 15 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 20%. This histogram indicates thegeneral absence of organoids associated with this colon cancer organoidline that are resistant to Docetaxel.

8. Colorectal Cancer

In this example, tissue culture wells containing a colorectal cancertumor organoid line were each exposed to one of the therapies listedabove.

FIG. 32A is a histogram summarizing the caspase 3/7 readout results forantimicrotubular chemotherapy Docetaxel (10,000 nM dose). Approximately35 organoids had 0% viability and were susceptible to the drug.Approximately 30 organoids had 100% viability and were resistant to thedrug. The black dotted line indicates that the mean viability for allorganoids in the plot is approximately 45%. This histogram indicates thepresence of organoids associated with this colorectal cancer organoidline that are resistant to Docetaxel, and may indicate that thiscolorectal cancer organoid line is more resistant than the colon cancerorganoid line in the example above.

FIG. 32B is a histogram summarizing the caspase 3/7 readout results forkinase inhibitor trametinib (10,000 nM dose). Approximately 50 organoidshad 0% viability and were susceptible to the drug. Approximately 10organoids had 100% viability and were resistant to the drug. The blackdotted line indicates that the mean viability for all organoids in theplot is approximately 25%. This histogram indicates the presence of asmall proportion of organoids associated with this colorectal cancerorganoid line that are resistant to trametinib.

In this colon TO line, sensitivity to drugs that inhibit MEK, a criticalcomponent of the mitogen-activated protein kinase (MAPK) pathway wasfound. The dose-response curve for trametinib, a potent MEK1 and MEK2inhibitor, showed a similar response for TO viability measured byTO-PRO-3, Caspase-3/7, and predicted TO viability from the RCA. Analysisof the molecular profile of the colon TO revealed a KRAS G12V missensemutation as well as a copy-number amplification of BRAF, both of whichare commonly associated with CRC and upregulate the MAPK pathway (Vaughnet al. 2011).

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

All references cited herein are incorporated herein by reference intheir entirety and for all purposes to the same extent as if eachindividual publication or patent or patent application was specificallyand individually indicated to be incorporated by reference in itsentirety for all purposes.

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a non-transitorycomputer readable storage medium. For instance, the computer programproduct could contain the program modules shown in any combination inFIG. 1 and/or as described elsewhere within the application. Theseprogram modules can be stored on a CD-ROM, DVD, magnetic disk storageproduct, USB key, or any other non-transitory computer readable data orprogram storage product.

The methods and systems described above may be utilized in combinationwith or as part of a digital and laboratory health care platform that isgenerally targeted to medical care and research. It should be understoodthat many uses of the methods and systems described above, incombination with such a platform, are possible. One example of such aplatform is described in U.S. patent application Ser. No. 16/657,804,titled “Data Based Cancer Research and Treatment Systems and Methods”,and filed Oct. 18, 2019, which is incorporated herein by reference andin its entirety for all purposes.

Many modifications and variations of this disclosure can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The embodiments were chosen anddescribed in order to best explain the principles of the invention andits practical applications, to thereby enable others skilled in the artto best utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. Thedisclosure is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled.

What is claimed is:
 1. A method of evaluating an effect of a therapeuticagent composition for a subject, the method comprising: A) exposing aplurality of tumor organoid subsets with a therapeutic agent compositioncomprising one or more therapeutic agents, wherein each of the tumororganoid subsets comprises at least five tumor organoids, wherein eachtumor organoid subset in the plurality of tumor organoid subsets is in adifferent well in one or more multi-well plates; wherein each tumororganoid subset in the plurality of tumor organoid subsets comprises aplurality of tumor organoids that are derived from one or more cells ofa tumor biopsy from the subject, and wherein each of the tumor organoidsubsets in the plurality of tumor organoid subsets is exposed to aparticular concentration of the therapeutic agent composition; B)exposing each respective tumor organoid subset in the plurality of tumororganoid subsets to one or more fluorescent markers; C) imaging theplurality of tumor organoid subsets thereby forming a plurality oftwo-dimensional pixelated digital fluorescent images; D) obtaining,using a computer system, for each respective tumor organoid subset inthe plurality of tumor organoid subsets, a corresponding tumor organoidprofile using numeric values for individual pixels within one or morecorresponding two-dimensional pixelated digital fluorescent images inthe plurality of two-dimensional pixelated digital fluorescent images,thereby obtaining a plurality of tumor organoid profiles, wherein eachrespective tumor organoid profile in the plurality of tumor organoidprofiles comprises a separate numeric cell viability value for each ofthe at least five tumor organoids in the respective tumor organoidsubset that corresponds to the respective tumor organoid profile; and E)assessing the effect of the therapeutic agent composition on each of thetumor organoids of the at least five tumor organoids in each of thetumor organoid subsets in the plurality of tumor organoid subsets basedon the separate numeric cell viability value for each tumor organoid ineach tumor organoid subset in the plurality of tumor organoid subsets.2. The method of claim 1, wherein the one or more fluorescent markerscomprises one or more cell death detection agents and a total celldetection agent.
 3. The method of claim 1, wherein the obtaining step D)comprises determining the proportion of numeric cell viability values ofa given tumor organoid subset that have greater than 1% cell viabilityafter contact with the therapeutic agent composition.
 4. The method ofclaim 3, wherein the assessing step E) comprises determining aprobability or a likelihood that the subject's tumor is resistant to thetherapeutic agent composition at a particular concentration based on theproportion of numeric cell viability values of a given tumor organoidsubset in the plurality of tumor organoid subsets that have greater than1% cell viability after contact with the therapeutic agent compositionat the particular concentration.
 5. The method of claim 4, wherein thesubject's tumor is determined to be likely resistant to the therapeuticagent composition at a particular concentration when 50% or more of thenumeric cell viability values for a tumor organoid subset in theplurality of tumor organoid subsets contacted with the therapeutic agentcomposition at the particular concentration are 50% or more viable. 6.The method of claim 4, wherein the subject's tumor is determined to belikely to be resistant to the therapeutic agent composition at aparticular concentration when 1% or more of the numeric cell viabilityvalues for a tumor organoid subset in the plurality of tumor organoidsubsets is contacted with the therapeutic agent composition at theparticular concentration are 100% or more viable.
 7. The method of claim4, wherein the subject's tumor is designated as likely to be resistantto the therapeutic agent composition and the method further comprisesrecommending to the subject a monitoring frequency that is more frequentthan a standard monitoring frequency.
 8. The method of claim 4, whereinthe subject's tumor is not designated as likely to be resistant to thetherapeutic agent composition and the method further comprisesrecommending to the subject a standard monitoring frequency.
 9. Themethod of claim 4, the method further comprising isolating a pluralityof tumor organoids from a tumor organoid subset that is designated aslikely to be resistant to the therapeutic agent composition andanalyzing the plurality of tumor organoids for one or more geneticvariants associated with resistance to the therapeutic agentcomposition.
 10. The method of-claim 4, the method further comprisingthe step of isolating a set of tumor organoids within the plurality oftumor organoid subsets having 0% cell viability and geneticallysequencing the isolated set of tumor organoids to detect one or moregenetic variants associated with at least 50% of the isolated tumororganoids, wherein one or more genetic variants associated with at least50% of the isolated set of tumor organoids is determined to beassociated with susceptibility to the therapeutic agent composition. 11.The method of claim 9, wherein the isolating is performed usingFicoll-Paque isolation.
 12. The method of claim 1, wherein the tumororganoid profile comprises a separate numeric cell viability value foreach of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400,500, 600, 700, 800, 900, or 1×10³, 1×10⁴, 1×10⁵, or 1×10⁶ tumororganoids in each of the tumor organoid subsets.
 13. The method of claim1, wherein the plurality of tumor organoid subsets comprises at least10, 15, 20, 25, 30, 35, 40, 50, 55, 60, 70, 80, 90, 100, 200, 300, 400,500, 600, 700, 800, 900, 1×10³, 1×10⁴, 1×10⁵, or 1×10⁶ tumor organoidsubsets.
 14. The method of claim 1, wherein each of at least two of thetumor organoid subsets is exposed to a unique concentration of thetherapeutic agent composition.
 15. The method of claim 1, wherein thetherapeutic agent composition comprises two or more therapeutic agents.16. The method of claim 1, wherein the subject has a basal cell skincancer, a squamous cancer, a breast cancer, a bladder cancer, a cervicalcancer, a colon cancer, an endometrial cancer, a head and neck cancer, ahepatobiliary cancer, a kidney cancer, a gastric cancer, a lung cancer,a mesothelial cancer of the pleural cavity, a mesothelial cancer of theperitoneal cavity, an ovarian cancer, prostate cancer, or a rectalcancer.
 17. The method of claim 1, wherein the method further comprises:F) exposing one or more tumor organoid subsets in the plurality of tumororganoid subsets that are designated as likely to be resistant to thetherapeutic agent composition to a second therapeutic agent composition;G) obtaining a second tumor organoid profile for each of the one or moretumor organoid subsets in step F), wherein each of the second tumororganoid profiles comprises a separate numeric cell viability value forat least five tumor organoids in the subset; and H) assessing an effectof the second therapeutic agent composition based on the second tumororganoid profiles.
 18. The method of claim 17, wherein the methodfurther comprises assigning a treatment regimen to the subject based onstep H).
 19. The method of claim 1, further comprising administering atherapy to the subject based on step E).
 20. The method of claim 1,wherein the therapeutic agent composition consists of one of thefollowing: a DNA damage response modulator, a cell cycle inhibitor, ametabolic inhibitor, a DNA synthesis inhibitor, an RNA synthesisinhibitor, a chemotherapy, an antimetabolite antineoplastic agent, anantimicrotubular antineoplastic agent, an antimetabolite antineoplasticagent, an antimitotic antineoplastic agent, an alkylating antineoplasticagent, a topoisomerase inhibitor, an apoptosis inducer inducers, akinase inhibitor, a proteasome inhibitor, a PARP inhibitor, a MEKinhibitor, an Akt inhibitor, an ATM/ATR inhibitor, a TGF-beta/Smadinhibitor, a HDAC inhibitor, or a retinoic acid receptor antagonist oragonist.
 21. The method of claim 1, wherein the assessing step E)comprises determining a sensitivity of the plurality of tumor organoidssubsets to the therapeutic agent composition, and wherein the methodfurther comprises: F) administering the therapeutic agent composition tothe subject when the therapeutic agent composition satisfies asensitivity threshold, and administering a therapy that is not thetherapeutic agent composition when the therapeutic agent compositiondoes not satisfy the sensitivity threshold, wherein the therapeuticagent composition comprises a PARP inhibitor.
 22. The method of claim 1,wherein each pixel in each pixelated digital fluorescent image in theplurality of two-dimensional pixelated digital fluorescent images has avalue between 0 and 2¹⁶.
 23. The method of claim 1, wherein the imagingC) comprises (i) taking a set of two or more two-dimensional pixelateddigital fluorescent images of a first tumor organoid subset in theplurality of tumor organoid subsets in the different well, wherein eachrespective image in the set of two-dimensional pixelated digitalfluorescent images of the first tumor organoid subset is at a differentZ-plane in a plurality of Z-planes, and (ii) projecting the set oftwo-dimensional pixelated digital fluorescent images into a singletwo-dimensional pixelated digital fluorescent image for the obtaining D)step.
 24. The method of claim 23 wherein each Z-plane is associated witha different submicron to 15 micron portion of a height in the differentwell.
 25. The method of claim 1, wherein the one or more fluorescentmarkers is a plurality of fluorescent markers and wherein eachtwo-dimensional pixelated digital fluorescent image in the plurality oftwo-dimensional pixelated digital fluorescent images is a multichannelimage.
 26. The method of claim 25, wherein the exposing B) exposes eachrespective tumor organoid subset in the plurality of tumor organoidsubsets with a first fluorescent marker, a second fluorescent marker,and a third fluorescent marker, wherein the first fluorescent marker isHoechst 33342, the second fluorescent marker is Caspase 3/7, and thethird fluorescent marker is TO-PRO-3, and wherein the multichannel imagecomprises a first channel for Hoechst 33342 fluorescence, a secondchannel for Caspase 3/7 fluorescence, and a third channel for TO-PRO-3fluorescence.
 27. The method of claim 25, wherein the exposing B)exposes each respective tumor organoid subset in the plurality of tumororganoid subsets to a first fluorescent marker, a second fluorescentmarker, and a third fluorescent marker and wherein the multichannelimage comprises a first channel for blue fluorescence, a second channelfor green fluorescence, and a third channel for red fluorescence.